2327705
Annie
2024-07-14
#import dataset
airline<-read.csv("/Users/anniefrancis/Desktop/airq402.dat.csv")
#initial data exploration
names(airline)## [1] "City1" "City2"
## [3] "Average.Flight.Fare" "Distance"
## [5] "Average.Weekly.Passengers" "Market.Leading.Airline..MLA."
## [7] "Market.Share.MLA" "Average.MLA.Fare"
## [9] "Low.Price.Airline..LPA." "Market.Share.LPA"
## [11] "Average.LPA.Fare"str(airline)## 'data.frame': 1000 obs. of 11 variables:
## $ City1 : chr "CAK" "CAK" "ALB" "ALB" ...
## $ City2 : chr "ATL" "MCO" "ATL" "BWI" ...
## $ Average.Flight.Fare : num 114.5 122.5 214.4 69.4 158.1 ...
## $ Distance : int 528 860 852 288 723 1204 2237 2467 1073 1130 ...
## $ Average.Weekly.Passengers : num 425 277 216 607 313 ...
## $ Market.Leading.Airline..MLA.: chr "FL" "FL" "DL" "WN" ...
## $ Market.Share.MLA : num 70.2 75.1 78.9 97 39.8 ...
## $ Average.MLA.Fare : num 111 123.1 224 68.9 161.4 ...
## $ Low.Price.Airline..LPA. : chr "FL" "DL" "CO" "WN" ...
## $ Market.Share.LPA : num 70.19 17.23 2.77 96.97 15.34 ...
## $ Average.LPA.Fare : num 111 118.9 167.1 68.9 145.4 ...#Univariate analysis(descriptive stats/distributions)
library(psych)
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphalibrary(DataExplorer)
describe(airline)## vars n mean sd median trimmed mad
## City1* 1 1000 36.67 23.28 32.00 35.66 26.69
## City2* 2 1000 51.20 22.36 53.50 52.40 28.91
## Average.Flight.Fare 3 1000 163.38 55.37 161.35 160.87 53.37
## Distance 4 1000 1056.97 643.20 919.00 990.12 598.23
## Average.Weekly.Passengers 5 1000 672.28 766.52 404.78 514.49 263.08
## Market.Leading.Airline..MLA.* 6 1000 9.15 5.36 11.00 9.41 5.93
## Market.Share.MLA 7 1000 60.13 19.37 59.24 59.76 20.01
## Average.MLA.Fare 8 1000 166.65 62.66 161.40 162.60 57.66
## Low.Price.Airline..LPA.* 9 1000 11.08 6.07 11.00 11.47 8.90
## Market.Share.LPA 10 1000 34.55 27.10 26.14 31.63 22.91
## Average.LPA.Fare 11 1000 143.19 47.23 137.82 140.00 40.79
## min max range skew kurtosis se
## City1* 1.00 90.00 89.00 0.27 -1.00 0.74
## City2* 1.00 85.00 84.00 -0.34 -0.92 0.71
## Average.Flight.Fare 50.52 401.23 350.71 0.52 0.35 1.75
## Distance 108.00 2724.00 2616.00 0.80 -0.32 20.34
## Average.Weekly.Passengers 181.41 8950.76 8769.35 4.42 30.16 24.24
## Market.Leading.Airline..MLA.* 1.00 16.00 15.00 -0.25 -1.56 0.17
## Market.Share.MLA 17.68 100.00 82.32 0.14 -0.68 0.61
## Average.MLA.Fare 50.52 490.03 439.51 0.83 1.42 1.98
## Low.Price.Airline..LPA.* 1.00 19.00 18.00 -0.28 -1.32 0.19
## Market.Share.LPA 1.06 100.00 98.94 0.82 -0.39 0.86
## Average.LPA.Fare 49.61 387.94 338.33 0.87 1.61 1.49plot_density(airline)
pairs.panels(airline)
plot_histogram(airline)
#Bi-variate/multi-variate analysis
plot_correlation(airline)## 2 features with more than 20 categories ignored!
## City1: 90 categories
## City2: 85 categories
#Outlier Detection
#Boxplots
# Drop the categorical columns
airplots <-airline[, !(names(airline) %in% c("City1", "City2", "Market.Leading.Airline..MLA.", "Low.Price.Airline..LPA."))]
boxplot(airplots)
#outliers are detected for Average Weekly Passengers
plot(airplots$Average.Weekly.Passengers)
#Performing normality test to check influence of outliers
shapiro.test(airline$Average.Weekly.Passengers)##
## Shapiro-Wilk normality test
##
## data: airline$Average.Weekly.Passengers
## W = 0.58645, p-value < 2.2e-16#p-value < 2.2e-16
#the output of shapiro.test() shows a p-value less than 0.05
#The data does not follow a normal distribution (reject H₀).variables_of_interest <- c("Average.Flight.Fare", "Distance", "Average.Weekly.Passengers",
"Market.Share.MLA", "Average.MLA.Fare",
"Market.Share.LPA", "Average.LPA.Fare")
df_subset <- airplots[, variables_of_interest]
# Create pairplot using pairs() function
pairs(df_subset)
#Average Flight fare is most corelated to fare of market leader and low performing flight with mild corelation with Distance as well
#Variabes need to be scaled before further analysis
#Transformation seems unnecessary as most data points don't show any clear trend with Average flight Fare.#removing outliers using the IQR method in R:
# Calculate IQR
Q1 <- quantile(airplots$Average.Weekly.Passengers, 0.25)
Q3 <- quantile(airplots$Average.Weekly.Passengers, 0.75)
IQR <- Q3 - Q1
# Identifying outliers using IQR method
outliers <- airplots[airplots$Average.Weekly.Passengers < (Q1 - 1.5 * IQR) |
airplots$Average.Weekly.Passengers > (Q3 + 1.5 * IQR), ]
#removing outliers
airline_clean <- airplots[!(airplots$Average.Weekly.Passengers < (Q1 - 1.5 * IQR) |
airplots$Average.Weekly.Passengers > (Q3 + 1.5 * IQR)), ]
#summary statistics after outlier treatment
summary(airline_clean$Average.Weekly.Passengers)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 181.4 249.4 375.3 501.4 652.5 1519.9plot(airline_clean$Average.Weekly.Passengers)
boxplot(airline_clean)
#outliers have been treated for Average.Weekly.Passengerslibrary(caret)## Loading required package: lattice#one hot encoding
#7 variables are continous variables and 4 variables are characters
#Hence, City1, City2, Market Leading Airline (MLA), and Low Price Airline (LPA)
# Assuming your dataset is stored in a data frame called 'df'
# Performing one-hot encoding on the specified columns
dummy_vars <- dummyVars(~City1+City2+Market.Leading.Airline..MLA.+Low.Price.Airline..LPA., data = airline)
one_hot_encoded <- predict(dummy_vars, newdata = airline)
# Converting the result to a data frame
airdata_encoded <- data.frame(one_hot_encoded)
#resulting data frame to check
print(airdata_encoded)## City1ABQ City1ACY City1ALB City1AMA City1ATL City1AUS City1BDL City1BHM
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## [ reached 'max' / getOption("max.print") -- omitted 524 rows ]# Dropping the original columns
data <-airline[, !(names(airline) %in% c("City1", "City2", "Market.Leading.Airline..MLA.", "Low.Price.Airline..LPA."))]
# Merging the one-hot encoded columns with the original dataset
airdata <- cbind(data, airdata_encoded)
#the final data frame to check
print(airdata)## Average.Flight.Fare Distance Average.Weekly.Passengers Market.Share.MLA
## 1 114.47 528 424.56 70.19
## 2 122.47 860 276.84 75.10
## 3 214.42 852 215.76 78.89
## 4 69.40 288 606.84 96.97
## 5 158.13 723 313.04 39.79
## 6 135.17 1204 199.02 40.68
## 7 152.85 2237 237.17 59.94
## 8 190.73 2467 191.95 17.89
## 9 129.35 1073 550.54 76.84
## 10 134.17 1130 202.93 35.40
## 11 212.49 1269 198.80 68.39
## 12 173.56 1670 312.39 49.16
## 13 170.67 1121 364.78 45.94
## 14 120.24 580 839.78 71.91
## 15 168.69 349 308.26 59.55
## 16 154.40 767 372.93 50.48
## 17 114.24 487 620.86 93.92
## 18 132.29 677 655.00 89.46
## 19 181.99 981 187.28 65.00
## 20 233.05 1825 344.45 31.33
## 21 162.21 889 388.15 88.63
## 22 161.74 1552 190.65 72.29
## 23 71.57 328 1252.39 77.65
## 24 163.63 1111 222.93 57.24
## 25 134.42 628 346.30 82.92
## 26 165.69 1180 284.34 47.82
## 27 77.82 321 191.19 86.29
## 28 227.93 1650 301.84 46.59
## 29 74.82 324 615.10 82.04
## 30 120.50 545 229.78 58.79
## 31 205.24 812 377.50 75.72
## 32 132.52 576 1453.91 67.61
## 33 156.55 946 1834.34 75.26
## 34 117.85 712 446.41 46.06
## 35 233.63 227 292.50 69.26
## 36 146.06 606 2885.76 40.28
## 37 234.20 373 294.78 94.80
## 38 184.90 554 455.65 58.01
## 39 195.72 446 379.56 85.99
## 40 147.50 732 2358.04 55.36
## 41 112.28 432 705.86 61.65
## 42 194.55 1199 1041.95 52.95
## 43 133.80 594 1141.52 48.45
## 44 118.93 644 251.41 92.69
## 45 132.00 581 1488.80 69.05
## 46 131.40 515 410.97 75.16
## 47 96.78 306 525.32 75.35
## 48 94.95 352 259.67 58.26
## 49 211.09 859 536.52 81.42
## 50 136.03 696 1453.04 52.49
## 51 208.60 432 385.32 82.99
## 52 94.79 270 828.80 79.46
## 53 110.60 692 848.04 72.60
## 54 221.95 1747 743.47 65.27
## 55 259.76 1946 1376.84 62.06
## 56 243.22 321 212.17 95.03
## 57 103.47 332 896.08 61.83
## 58 129.21 595 1333.47 54.27
## 59 103.19 669 678.91 47.69
## 60 138.67 906 1036.08 42.55
## 61 209.89 214 220.21 91.06
## 62 116.41 425 950.00 76.30
## 63 98.62 508 370.54 83.60
## 64 150.38 761 6185.43 64.65
## 65 130.89 516 384.56 89.88
## 66 227.23 2130 249.13 42.93
## 67 198.07 761 209.45 79.29
## 68 199.73 821 196.08 64.41
## 69 210.45 1900 302.28 51.56
## 70 137.90 403 1434.34 60.98
## 71 92.18 272 206.63 65.75
## 72 130.36 665 1995.43 53.12
## 73 236.93 1587 697.82 50.51
## 74 102.75 526 888.58 43.03
## 75 252.66 2172 347.60 67.19
## 76 203.29 903 245.54 82.86
## 77 98.23 356 1179.78 75.50
## 78 195.29 481 372.50 93.55
## 79 115.95 749 356.95 45.18
## 80 236.10 2092 285.10 57.60
## 81 192.67 483 633.80 49.92
## 82 231.06 1589 460.32 67.55
## 83 206.36 874 412.17 78.45
## 84 250.88 1891 522.17 67.98
## 85 299.31 2139 789.67 62.75
## 86 285.76 2116 285.65 58.59
## 87 292.00 1919 329.23 63.65
## 88 82.76 215 342.28 91.23
## 89 265.27 2182 620.54 63.35
## 90 95.19 223 189.56 70.29
## 91 128.75 406 1486.52 70.94
## 92 156.32 547 2558.47 74.26
## 93 137.63 545 537.93 89.53
## 94 110.61 781 207.39 65.40
## 95 128.20 977 431.41 99.72
## 96 127.37 982 290.76 99.14
## 97 123.98 852 444.23 97.01
## 98 122.38 914 208.36 95.77
## 99 186.26 1340 357.71 48.86
## 100 176.77 972 589.78 66.95
## 101 90.70 183 1266.73 79.11
## 102 150.14 768 453.26 44.74
## 103 117.90 525 347.93 95.87
## 104 87.71 160 595.97 73.17
## 105 138.96 1085 500.10 72.00
## 106 178.55 1238 508.04 55.99
## 107 92.35 335 267.06 96.41
## 108 258.83 1035 207.50 69.35
## 109 132.70 753 242.28 82.81
## 110 134.21 446 194.45 68.47
## 111 271.07 1519 656.95 43.58
## 112 163.37 995 245.54 39.88
## 113 133.04 868 461.41 48.73
## 114 177.41 1161 186.95 46.04
## 115 153.38 717 213.04 73.36
## 116 170.03 1161 252.06 50.02
## 117 223.26 1471 483.15 75.86
## 118 177.23 1764 241.08 30.43
## 119 245.59 1313 313.91 38.95
## 120 122.89 682 386.08 80.46
## 121 99.16 370 1162.71 71.51
## 122 68.57 281 671.19 94.94
## 123 237.00 360 198.04 93.90
## 124 129.98 621 1870.86 40.54
## 125 83.51 314 945.00 52.04
## 126 80.62 336 483.26 47.09
## 127 216.89 1217 782.06 66.00
## 128 191.68 1491 743.69 64.22
## 129 198.83 408 470.10 72.25
## 130 129.48 925 1037.60 55.67
## 131 139.06 919 252.60 68.20
## 132 74.41 283 848.36 95.34
## 133 188.43 1246 857.39 53.32
## 134 124.00 515 392.39 75.40
## 135 121.07 663 485.54 67.94
## 136 126.27 966 442.39 59.75
## 137 156.54 2106 1018.26 42.55
## 138 72.03 220 490.32 99.88
## 139 196.95 2329 1134.02 35.30
## 140 86.77 495 565.97 89.16
## 141 74.08 377 1299.56 96.61
## 142 144.45 946 537.50 56.94
## 143 242.02 936 347.82 73.40
## 144 103.89 587 884.78 90.15
## 145 142.22 998 540.54 46.55
## 146 71.81 159 194.02 98.09
## 147 173.01 2446 322.50 47.52
## 148 161.38 1180 189.89 29.82
## 149 177.45 2283 231.63 26.65
## 150 123.67 787 1519.89 56.01
## 151 165.32 1998 938.04 39.40
## 152 180.99 2358 309.78 24.14
## 153 73.42 328 1545.32 97.30
## 154 77.15 255 510.54 90.35
## 155 78.49 277 260.21 92.52
## 156 167.57 2395 307.82 31.60
## 157 134.54 737 722.06 51.34
## 158 180.52 1864 387.93 36.70
## 159 173.49 1407 432.39 42.15
## 160 181.94 2295 701.30 25.13
## 161 266.31 2457 413.91 70.27
## 162 186.18 2438 265.00 33.38
## 163 176.63 2335 495.32 17.68
## 164 125.09 842 1071.63 55.01
## 165 172.16 1976 214.34 34.33
## 166 137.32 883 386.95 72.52
## 167 132.62 584 336.19 76.13
## 168 170.24 597 249.02 48.31
## 169 135.91 570 318.36 54.83
## 170 156.02 1618 192.39 59.83
## 171 76.70 321 323.36 94.85
## 172 213.08 866 343.91 60.11
## 173 110.20 478 307.17 68.64
## 174 109.96 460 270.54 82.28
## 175 141.26 649 244.02 48.06
## 176 99.44 520 261.84 85.26
## 177 126.18 674 275.32 47.76
## 178 133.52 735 251.95 62.64
## 179 80.47 344 656.95 55.27
## 180 76.18 291 251.30 60.12
## 181 82.23 399 844.45 56.30
## 182 72.50 287 354.67 76.55
## 183 228.64 727 290.54 78.48
## 184 179.60 867 2090.76 42.23
## 185 249.21 752 270.00 87.39
## 186 252.85 563 315.97 86.99
## 187 185.06 640 307.93 57.99
## 188 248.60 1562 1098.04 61.55
## 189 108.11 709 214.67 33.41
## 190 213.21 1754 930.10 64.35
## 191 237.89 632 542.28 81.77
## 192 144.67 1237 1254.56 67.45
## 193 148.71 1249 723.15 77.93
## 194 236.58 1609 667.17 68.01
## 195 141.77 1011 208.04 50.57
## 196 167.56 1256 265.43 35.70
## 197 170.57 2381 865.65 43.54
## 198 314.01 2611 1259.02 38.28
## 199 191.79 1139 188.69 57.60
## 200 154.36 1258 992.50 81.80
## 201 186.23 860 283.04 67.35
## 202 270.65 1124 717.71 69.84
## 203 157.76 1367 310.00 41.97
## 204 128.94 200 4662.82 45.94
## 205 131.65 1121 1757.06 66.47
## 206 204.13 280 1115.54 76.66
## 207 184.68 2300 594.56 37.02
## 208 206.06 496 514.23 93.36
## 209 243.79 2537 242.17 30.25
## 210 172.27 612 504.78 64.57
## 211 193.78 1046 414.45 82.34
## 212 254.38 2105 270.86 60.59
## 213 307.42 2588 355.21 46.69
## 214 355.15 2704 1270.00 45.88
## 215 286.14 2689 505.76 61.05
## 216 291.35 2592 183.04 37.47
## 217 258.68 2496 500.76 27.32
## 218 146.73 1193 877.71 79.95
## 219 163.37 413 2569.56 63.04
## 220 170.77 1197 593.04 68.85
## 221 187.62 473 289.34 49.24
## 222 171.26 1212 186.41 51.25
## 223 119.43 1166 309.78 28.66
## 224 146.87 1987 365.32 52.87
## 225 173.89 2217 207.71 25.64
## 226 89.10 301 1352.71 76.19
## 227 117.41 1011 563.15 41.90
## 228 155.42 1912 232.17 59.03
## 229 106.53 1054 385.86 27.80
## 230 222.11 1243 216.08 80.53
## 231 62.88 223 1962.50 97.67
## 232 78.93 325 2542.28 99.61
## 233 73.73 369 1074.78 70.73
## 234 130.69 817 479.78 73.44
## 235 99.45 374 195.54 79.54
## 236 80.33 358 1565.32 99.22
## 237 128.20 326 551.41 99.84
## 238 80.67 296 1129.23 99.96
## 239 145.58 937 724.02 78.59
## 240 92.89 267 314.67 76.44
## 241 178.94 641 361.19 50.88
## 242 137.63 599 854.78 33.03
## 243 239.16 937 446.63 57.65
## 244 206.62 1338 296.30 52.53
## 245 232.15 500 266.19 53.40
## 246 170.45 632 230.76 78.70
## 247 266.44 921 229.56 42.23
## 248 174.90 1917 272.50 53.84
## 249 216.47 2125 453.26 51.31
## 250 164.82 651 257.28 78.28
## 251 206.31 930 263.15 51.92
## 252 247.41 543 1129.78 72.70
## 253 191.79 468 285.86 91.14
## 254 257.32 447 377.82 94.33
## 255 210.99 1774 220.76 48.00
## 256 199.95 575 214.23 50.58
## 257 246.26 2296 321.73 59.42
## 258 198.95 508 256.52 84.40
## 259 233.30 330 356.84 95.58
## 260 265.86 264 390.65 48.10
## 261 94.32 316 1946.63 37.46
## 262 91.50 296 1426.95 41.33
## 263 190.85 802 2621.63 60.96
## 264 100.47 240 378.91 53.32
## 265 154.27 895 2353.15 54.05
## 266 147.08 306 203.58 66.63
## 267 92.55 235 2262.71 39.95
## 268 192.71 1237 223.36 78.05
## 269 135.72 1182 2240.65 24.41
## 270 132.67 1120 1339.13 46.15
## 271 188.73 590 219.13 66.91
## 272 192.69 594 206.63 61.38
## 273 149.81 783 942.17 38.83
## 274 175.06 945 1660.10 41.51
## 275 83.81 177 541.73 40.38
## 276 140.35 677 198.15 66.81
## 277 161.49 865 278.58 42.52
## 278 77.80 405 1912.93 51.06
## 279 132.86 1521 3149.13 23.04
## 280 135.63 552 251.84 56.58
## 281 124.86 773 300.00 93.87
## 282 167.07 1750 4120.00 39.15
## 283 79.86 286 703.58 68.05
## 284 151.27 843 533.91 55.78
## 285 190.76 491 315.21 52.89
## 286 178.44 1197 1273.58 41.16
## 287 161.71 349 2536.19 55.64
## 288 88.02 409 1046.63 70.91
## 289 163.41 837 777.17 36.79
## 290 175.42 740 7442.06 34.00
## 291 140.64 717 243.26 34.13
## 292 173.78 1844 544.13 54.51
## 293 158.10 693 211.95 69.53
## 294 84.05 423 905.43 58.29
## 295 208.23 1706 189.34 24.28
## 296 127.46 1005 3042.82 35.28
## 297 172.15 678 2142.50 34.35
## 298 132.81 1444 3018.80 25.36
## 299 171.88 412 766.19 49.96
## 300 221.50 1751 594.34 59.54
## 301 141.52 849 770.97 35.64
## 302 119.66 647 875.10 57.98
## 303 177.35 1680 191.73 64.28
## 304 205.77 642 274.45 60.03
## 305 192.97 528 249.45 52.89
## 306 210.03 1790 393.58 60.03
## 307 84.73 258 1876.08 45.68
## 308 214.32 1258 482.82 38.67
## 309 188.15 1041 536.73 48.68
## 310 160.71 1728 1441.41 37.08
## 311 187.80 1854 2479.45 58.76
## 312 174.72 1838 756.84 38.21
## 313 181.88 1731 879.78 52.16
## 314 132.90 1050 245.86 73.78
## 315 179.94 1733 1415.43 46.69
## 316 186.83 607 199.78 55.22
## 317 130.18 1012 2118.69 25.01
## 318 175.61 1440 356.84 70.51
## 319 144.02 585 238.80 75.64
## 320 190.71 612 2571.41 57.44
## 321 188.71 1144 352.28 51.06
## 322 219.66 738 525.76 52.84
## 323 287.35 812 263.15 65.67
## 324 230.74 1069 224.67 77.26
## 325 174.95 932 228.04 87.08
## 326 173.51 879 184.02 92.91
## 327 238.60 886 193.58 54.40
## 328 211.49 1678 205.00 82.13
## 329 321.40 1900 251.08 75.67
## 330 251.16 596 251.30 47.92
## 331 223.58 589 885.00 77.70
## 332 181.38 756 286.52 88.96
## 333 157.78 773 347.28 92.42
## 334 194.70 411 380.65 93.94
## 335 258.85 1021 303.36 61.30
## 336 219.13 1201 310.10 47.31
## 337 159.37 1062 446.73 62.40
## 338 178.31 1025 313.69 83.47
## 339 200.44 475 190.00 64.47
## 340 217.88 1107 431.41 75.78
## 341 149.38 694 204.67 61.17
## 342 158.70 1825 832.71 78.16
## 343 209.44 2053 579.45 66.83
## 344 181.70 1080 202.50 48.41
## 345 254.99 622 298.15 61.24
## 346 84.31 448 416.73 72.84
## 347 231.15 425 1052.39 80.03
## 348 146.93 895 514.13 61.96
## 349 167.72 1737 473.26 45.59
## 350 200.39 540 187.17 64.45
## 351 90.58 487 510.32 46.62
## 352 184.75 2026 224.67 24.67
## 353 274.75 2161 249.34 42.63
## 354 215.13 2021 192.71 22.95
## 355 155.57 932 443.15 61.19
## 356 221.19 310 213.15 95.00
## 357 142.89 603 225.00 94.15
## 358 121.42 604 285.32 54.89
## 359 180.26 833 217.71 49.52
## 360 186.15 617 234.67 48.95
## 361 152.57 927 542.17 70.50
## 362 212.25 1155 229.02 56.52
## 363 141.32 972 263.91 42.00
## 364 151.41 930 203.80 59.62
## 365 131.81 550 196.84 53.94
## 366 181.13 1001 276.73 72.23
## 367 140.39 1772 733.69 55.20
## 368 166.38 1996 459.89 33.06
## 369 239.85 627 239.56 80.35
## 370 166.16 483 876.19 35.19
## 371 110.12 802 644.23 32.63
## 372 164.30 405 365.43 59.51
## 373 149.65 1671 571.08 46.85
## 374 82.44 410 334.56 50.00
## 375 152.25 1964 243.69 18.37
## 376 177.79 2121 218.47 25.82
## 377 157.29 2017 183.58 26.46
## 378 119.91 829 482.71 51.40
## 379 106.78 354 364.67 67.71
## 380 87.45 201 306.08 66.54
## 381 196.27 861 223.58 63.29
## 382 155.99 650 1488.69 47.02
## 383 231.60 987 722.50 51.97
## 384 109.72 562 893.80 67.93
## 385 212.91 1119 506.30 48.19
## 386 178.49 999 188.36 50.60
## 387 122.55 461 322.17 98.00
## 388 277.25 1471 322.93 62.23
## 389 86.02 252 4075.43 71.68
## 390 190.66 762 431.19 63.95
## 391 181.34 919 335.10 49.56
## 392 159.24 461 768.80 94.30
## 393 162.86 1067 1173.91 58.75
## 394 83.31 304 612.93 71.64
## 395 204.22 1220 494.13 97.95
## 396 240.12 1246 1266.52 52.87
## 397 208.03 733 229.56 65.29
## 398 77.76 293 770.97 85.36
## 399 186.63 432 299.23 56.95
## 400 223.28 1121 530.21 81.44
## 401 78.14 319 534.78 86.56
## 402 184.36 853 342.39 46.69
## 403 235.76 853 800.21 39.03
## 404 224.98 631 421.52 69.64
## 405 107.28 448 1199.89 65.80
## 406 248.32 1391 3261.41 61.28
## 407 191.54 1213 206.30 74.18
## 408 269.76 1468 283.15 58.42
## 409 83.35 181 375.32 72.98
## 410 206.80 585 235.00 81.45
## 411 214.23 1200 323.15 51.69
## 412 189.56 984 1009.89 55.45
## 413 226.96 1302 1006.30 56.41
## 414 193.73 879 1091.95 51.27
## 415 223.85 1068 384.78 44.88
## 416 238.67 1626 376.73 45.23
## 417 207.45 1062 522.06 62.60
## 418 227.12 1158 235.76 81.60
## 419 224.54 1442 328.58 66.78
## 420 152.58 551 826.30 92.56
## 421 223.86 999 422.71 39.67
## 422 84.77 248 1695.32 77.29
## 423 250.47 1182 615.00 59.91
## 424 283.90 1476 805.86 55.35
## 425 288.99 1450 532.06 74.60
## 426 215.22 1216 580.54 69.98
## 427 254.29 1670 734.67 50.06
## 428 194.21 929 730.21 53.85
## 429 210.98 824 198.58 81.44
## 430 80.26 237 705.65 69.93
## 431 256.12 1192 1594.13 71.77
## 432 201.21 1103 211.08 75.54
## 433 173.97 1925 200.54 29.53
## 434 163.70 554 235.76 44.81
## 435 132.14 808 228.36 43.21
## 436 122.52 1123 862.60 34.52
## 437 137.78 1703 809.67 47.25
## 438 209.57 1671 217.50 33.88
## 439 151.25 889 1213.69 51.88
## 440 140.31 977 525.65 49.91
## 441 157.93 533 658.80 52.03
## 442 141.29 629 1350.54 37.57
## 443 180.55 862 1857.71 56.99
## 444 270.89 1709 271.84 53.21
## 445 176.27 895 278.91 47.11
## 446 143.91 680 1166.19 44.64
## 447 162.72 1062 414.56 38.85
## 448 209.70 1626 2326.19 47.79
## 449 187.48 957 378.47 90.86
## 450 141.59 495 259.02 58.70
## 451 161.33 472 328.04 55.26
## 452 159.98 819 344.23 76.94
## 453 189.68 1545 909.89 31.57
## 454 245.67 1557 583.26 44.57
## 455 165.91 602 1495.86 33.01
## 456 239.88 1290 247.17 62.97
## 457 178.10 992 671.08 48.85
## 458 175.06 1436 296.19 37.83
## 459 146.57 804 258.15 52.84
## 460 178.05 910 500.65 61.87
## Average.MLA.Fare Market.Share.LPA Average.LPA.Fare City1ABQ City1ACY
## 1 111.03 70.19 111.03 0 0
## 2 123.09 17.23 118.94 0 0
## 3 223.98 2.77 167.12 0 0
## 4 68.86 96.97 68.86 0 0
## 5 161.36 15.34 145.42 0 0
## 6 137.97 17.09 127.69 0 0
## 7 148.59 59.94 148.59 0 0
## 8 205.06 16.59 174.00 0 0
## 9 127.69 76.84 127.69 0 0
## 10 132.91 26.40 124.78 0 0
## 11 226.79 11.91 200.93 1 0
## 12 180.49 14.37 161.39 1 0
## 13 174.62 33.87 163.22 1 0
## 14 117.20 71.91 117.20 1 0
## 15 181.34 39.95 149.41 1 0
## 16 152.03 50.48 152.03 1 0
## 17 113.82 93.92 113.82 1 0
## 18 130.44 89.46 130.44 1 0
## 19 182.27 2.37 109.14 1 0
## 20 233.26 19.50 222.08 1 0
## 21 164.27 7.19 144.51 1 0
## 22 151.81 72.29 151.81 1 0
## 23 70.99 77.65 70.99 1 0
## 24 167.46 20.77 155.82 1 0
## 25 136.70 13.05 119.03 1 0
## 26 164.81 25.07 159.64 1 0
## 27 73.50 86.29 73.50 1 0
## 28 213.13 46.59 213.13 1 0
## 29 71.51 82.04 71.51 0 0
## 30 119.76 37.55 118.05 0 0
## 31 212.59 10.36 193.01 0 0
## 32 138.42 26.56 118.54 0 0
## 33 163.05 16.29 128.51 0 0
## 34 123.55 46.01 110.49 0 0
## 35 234.85 30.02 229.85 0 0
## 36 161.88 26.08 111.32 0 0
## 37 236.93 1.14 113.83 0 0
## 38 183.47 58.01 183.47 0 0
## 39 202.58 1.68 124.71 0 0
## 40 152.80 33.45 143.65 0 0
## 41 117.78 36.51 101.60 0 0
## 42 200.28 13.18 176.86 0 0
## 43 135.25 47.64 132.35 0 0
## 44 116.95 92.69 116.95 0 0
## 45 135.95 28.59 122.01 0 0
## 46 133.36 22.34 125.97 0 0
## 47 105.10 22.34 67.42 0 0
## 48 88.61 58.26 88.61 0 0
## 49 223.92 3.93 111.97 0 0
## 50 139.61 14.38 118.58 0 0
## 51 221.13 4.25 85.82 0 0
## 52 97.25 20.00 84.17 0 0
## 53 111.68 15.21 94.72 0 0
## 54 249.17 18.28 175.02 0 0
## 55 285.55 1.93 156.00 0 0
## 56 246.77 1.38 135.33 0 0
## 57 108.06 18.69 89.41 0 0
## 58 136.86 29.54 118.17 0 0
## 59 106.11 16.12 88.14 0 0
## 60 140.55 11.83 125.63 0 0
## 61 211.42 7.79 194.47 0 0
## 62 118.78 21.87 107.82 0 0
## 63 95.58 83.60 95.58 0 0
## 64 159.12 16.55 124.12 0 0
## 65 132.18 7.99 107.50 0 0
## 66 236.56 28.22 190.35 0 0
## 67 202.85 10.32 198.04 0 0
## 68 215.05 7.15 135.42 0 0
## 69 218.76 11.29 191.57 0 0
## 70 147.26 37.88 122.39 0 0
## 71 95.28 32.82 82.54 0 0
## 72 138.50 13.78 114.65 0 0
## 73 255.09 35.45 225.74 0 0
## 74 107.84 24.00 95.83 0 0
## 75 256.53 2.62 148.69 0 0
## 76 205.62 9.03 164.20 0 0
## 77 104.32 22.19 76.06 0 0
## 78 197.86 4.31 141.93 0 0
## 79 119.71 44.70 108.15 0 0
## 80 242.22 13.34 213.91 0 0
## 81 195.33 45.27 192.04 0 0
## 82 267.69 9.53 128.00 0 0
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## [ reached 'max' / getOption("max.print") -- omitted 540 rows ]str(airdata)## 'data.frame': 1000 obs. of 217 variables:
## $ Average.Flight.Fare : num 114.5 122.5 214.4 69.4 158.1 ...
## $ Distance : int 528 860 852 288 723 1204 2237 2467 1073 1130 ...
## $ Average.Weekly.Passengers : num 425 277 216 607 313 ...
## $ Market.Share.MLA : num 70.2 75.1 78.9 97 39.8 ...
## $ Average.MLA.Fare : num 111 123.1 224 68.9 161.4 ...
## $ Market.Share.LPA : num 70.19 17.23 2.77 96.97 15.34 ...
## $ Average.LPA.Fare : num 111 118.9 167.1 68.9 145.4 ...
## $ City1ABQ : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ACY : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ALB : num 0 0 1 1 1 1 1 1 1 1 ...
## $ City1AMA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ATL : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1AUS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BDL : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BHM : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BNA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BOI : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BOS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BTV : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BUF : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BUR : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1BWI : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1CAE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1CAK : num 1 1 0 0 0 0 0 0 0 0 ...
## $ City1CHS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1CLE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1CLT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1CMH : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1COS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1CRP : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1CVG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1DAY : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1DEN : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1DFW : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1DSM : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1DTW : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ELP : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1EUG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1FLL : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1FNT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1GPT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1GRR : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1GSO : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1GSP : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1HRL : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1IAD : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1IAH : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1IND : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ISP : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1JAX : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1LAS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1LAX : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1LGA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1LGB : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1LIT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MCI : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MCO : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MDT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MEM : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MHT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MIA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MKE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MSN : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MSP : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MSY : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1MYR : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1OAK : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1OKC : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1OMA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ONT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ORD : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ORF : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1PDX : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1PHF : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1PHL : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1PHX : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1PIT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1PSP : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1PVD : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1RDU : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1RNO : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1ROC : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1RSW : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SAN : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SAT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SDF : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SEA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SFO : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SJC : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SLC : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SMF : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SNA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1STL : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1SYR : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1TLH : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1TPA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City1TYS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ City2ATL : num 1 0 1 0 0 0 0 0 0 0 ...
## $ City2AUS : num 0 0 0 0 0 0 0 0 0 0 ...
## [list output truncated]summary(airdata)## Average.Flight.Fare Distance Average.Weekly.Passengers
## Min. : 50.52 Min. : 108.0 Min. : 181.4
## 1st Qu.:125.97 1st Qu.: 553.5 1st Qu.: 257.2
## Median :161.34 Median : 919.0 Median : 404.8
## Mean :163.38 Mean :1057.0 Mean : 672.3
## 3rd Qu.:197.39 3rd Qu.:1452.5 3rd Qu.: 769.9
## Max. :401.23 Max. :2724.0 Max. :8950.8
## Market.Share.MLA Average.MLA.Fare Market.Share.LPA Average.LPA.Fare
## Min. : 17.68 Min. : 50.52 Min. : 1.06 Min. : 49.61
## 1st Qu.: 46.51 1st Qu.:124.08 1st Qu.: 13.20 1st Qu.:113.77
## Median : 59.24 Median :161.40 Median : 26.14 Median :137.82
## Mean : 60.13 Mean :166.65 Mean : 34.55 Mean :143.19
## 3rd Qu.: 73.51 3rd Qu.:202.98 3rd Qu.: 52.97 3rd Qu.:168.53
## Max. :100.00 Max. :490.03 Max. :100.00 Max. :387.94
## City1ABQ City1ACY City1ALB City1AMA
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.018 Mean :0.004 Mean :0.008 Mean :0.002
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1ATL City1AUS City1BDL City1BHM
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.064 Mean :0.021 Mean :0.014 Mean :0.008
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1BNA City1BOI City1BOS City1BTV
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.011 Mean :0.008 Mean :0.038 Mean :0.001
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1BUF City1BUR City1BWI City1CAE City1CAK
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000 Median :0.000 Median :0.000
## Mean :0.009 Mean :0.01 Mean :0.047 Mean :0.001 Mean :0.002
## 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000 Max. :1.000
## City1CHS City1CLE City1CLT City1CMH
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.001 Mean :0.022 Mean :0.018 Mean :0.018
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1COS City1CRP City1CVG City1DAY
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.003 Mean :0.002 Mean :0.012 Mean :0.003
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1DEN City1DFW City1DSM City1DTW
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.036 Mean :0.052 Mean :0.002 Mean :0.033
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1ELP City1EUG City1FLL City1FNT
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.006 Mean :0.001 Mean :0.029 Mean :0.001
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1GPT City1GRR City1GSO City1GSP
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.001 Mean :0.004 Mean :0.003 Mean :0.001
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1HRL City1IAD City1IAH City1IND
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.001 Mean :0.001 Mean :0.038 Mean :0.012
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1ISP City1JAX City1LAS City1LAX
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.005 Mean :0.007 Mean :0.037 Mean :0.031
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1LGA City1LGB City1LIT City1MCI
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.028 Mean :0.004 Mean :0.001 Mean :0.023
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1MCO City1MDT City1MEM City1MHT
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.017 Mean :0.001 Mean :0.006 Mean :0.003
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1MIA City1MKE City1MSN City1MSP City1MSY
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.00 Median :0.000
## Mean :0.013 Mean :0.007 Mean :0.001 Mean :0.02 Mean :0.015
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.00 Max. :1.000
## City1MYR City1OAK City1OKC City1OMA
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.001 Mean :0.012 Mean :0.002 Mean :0.004
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1ONT City1ORD City1ORF City1PDX
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.008 Mean :0.063 Mean :0.003 Mean :0.011
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1PHF City1PHL City1PHX City1PIT
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.001 Mean :0.011 Mean :0.018 Mean :0.003
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1PSP City1PVD City1RDU City1RNO
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.003 Mean :0.005 Mean :0.005 Mean :0.003
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1ROC City1RSW City1SAN City1SAT
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.001 Mean :0.008 Mean :0.006 Mean :0.003
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1SDF City1SEA City1SFO City1SJC
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.004 Mean :0.004 Mean :0.004 Mean :0.004
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1SLC City1SMF City1SNA City1STL
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.007 Mean :0.006 Mean :0.001 Mean :0.009
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City1SYR City1TLH City1TPA City1TYS
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.001 Mean :0.001 Mean :0.002 Mean :0.001
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2ATL City2AUS City2BDL City2BHM City2BNA
## Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.00 Median :0.000 Median :0.000
## Mean :0.003 Mean :0.001 Mean :0.01 Mean :0.001 Mean :0.016
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000
## City2BOS City2BUF City2BWI City2CLE
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.002 Mean :0.002 Mean :0.004 Mean :0.004
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2CLT City2CMH City2CVG City2DAY
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.003 Mean :0.004 Mean :0.003 Mean :0.004
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2DEN City2DFW City2DSM City2DTW
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.012 Mean :0.016 Mean :0.001 Mean :0.007
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2ELP City2FLL City2FNT City2GEG
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.003 Mean :0.014 Mean :0.001 Mean :0.009
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2GPT City2GSO City2HPN City2HRL City2IAD
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00
## Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0.00
## Mean :0.001 Mean :0.003 Mean :0.001 Mean :0.001 Mean :0.04
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.00
## City2IAH City2ICT City2IND City2ISP City2JAN
## Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.00 Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.02 Mean :0.001 Mean :0.009 Mean :0.003 Mean :0.002
## 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.00 Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2JAX City2LAS City2LAX City2LBB
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.008 Mean :0.026 Mean :0.027 Mean :0.003
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2LGA City2LGB City2LIT City2MAF
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.042 Mean :0.002 Mean :0.003 Mean :0.002
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2MCI City2MCO City2MDT City2MEM
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.011 Mean :0.043 Mean :0.001 Mean :0.007
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2MHT City2MIA City2MKE City2MSP City2MSY
## Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.00 Median :0.000 Median :0.000
## Mean :0.005 Mean :0.012 Mean :0.01 Mean :0.025 Mean :0.019
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000
## City2OAK City2OKC City2OMA City2ONT City2ORD
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.00 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000 Median :0.00 Median :0.000
## Mean :0.016 Mean :0.01 Mean :0.008 Mean :0.01 Mean :0.009
## 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.00 Max. :1.000
## City2ORF City2PBI City2PDX City2PHF
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.008 Mean :0.014 Mean :0.022 Mean :0.001
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2PHL City2PHX City2PIT City2PNS
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.023 Mean :0.036 Mean :0.017 Mean :0.001
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2PVD City2PWM City2RDU City2RIC
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.012 Mean :0.001 Mean :0.023 Mean :0.004
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2RNO City2ROC City2RSW City2SAN
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.009 Mean :0.005 Mean :0.009 Mean :0.036
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2SAT City2SAV City2SDF City2SEA
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.018 Mean :0.002 Mean :0.006 Mean :0.042
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2SFO City2SJC City2SLC City2SMF City2SNA
## Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.00 Median :0.000 Median :0.000
## Mean :0.031 Mean :0.022 Mean :0.02 Mean :0.017 Mean :0.019
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000
## City2SRQ City2STL City2SYR City2TLH
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.002 Mean :0.031 Mean :0.003 Mean :0.002
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## City2TPA City2TUL City2TUS Market.Leading.Airline..MLA.AA
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000 Median :0.000
## Mean :0.046 Mean :0.006 Mean :0.012 Mean :0.135
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.000
## Market.Leading.Airline..MLA.AS Market.Leading.Airline..MLA.B6
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.032 Mean :0.014
## 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## Market.Leading.Airline..MLA.CO Market.Leading.Airline..MLA.DL
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.069 Mean :0.162
## 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## Market.Leading.Airline..MLA.F9 Market.Leading.Airline..MLA.FL
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.003 Mean :0.016
## 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## Market.Leading.Airline..MLA.G4 Market.Leading.Airline..MLA.HP
## Min. :0.000 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.00
## Median :0.000 Median :0.00
## Mean :0.001 Mean :0.03
## 3rd Qu.:0.000 3rd Qu.:0.00
## Max. :1.000 Max. :1.00
## Market.Leading.Airline..MLA.NK Market.Leading.Airline..MLA.NW
## Min. :0.00 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000
## Median :0.00 Median :0.000
## Mean :0.01 Mean :0.086
## 3rd Qu.:0.00 3rd Qu.:0.000
## Max. :1.00 Max. :1.000
## Market.Leading.Airline..MLA.TZ Market.Leading.Airline..MLA.UA
## Min. :0.00 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000
## Median :0.00 Median :0.000
## Mean :0.01 Mean :0.091
## 3rd Qu.:0.00 3rd Qu.:0.000
## Max. :1.00 Max. :1.000
## Market.Leading.Airline..MLA.US Market.Leading.Airline..MLA.WN
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.068 Mean :0.257
## 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## Market.Leading.Airline..MLA.YX Low.Price.Airline..LPA.AA
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.016 Mean :0.129
## 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## Low.Price.Airline..LPA.AQ Low.Price.Airline..LPA.AS Low.Price.Airline..LPA.AX
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.002 Mean :0.027 Mean :0.003
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000
## Low.Price.Airline..LPA.B6 Low.Price.Airline..LPA.CO Low.Price.Airline..LPA.DL
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.015 Mean :0.045 Mean :0.151
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000
## Low.Price.Airline..LPA.F9 Low.Price.Airline..LPA.FL Low.Price.Airline..LPA.G4
## Min. :0.00 Min. :0.00 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.000
## Median :0.00 Median :0.00 Median :0.000
## Mean :0.03 Mean :0.06 Mean :0.001
## 3rd Qu.:0.00 3rd Qu.:0.00 3rd Qu.:0.000
## Max. :1.00 Max. :1.00 Max. :1.000
## Low.Price.Airline..LPA.HP Low.Price.Airline..LPA.NK Low.Price.Airline..LPA.NW
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.058 Mean :0.016 Mean :0.033
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000
## Low.Price.Airline..LPA.SY Low.Price.Airline..LPA.TZ Low.Price.Airline..LPA.UA
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.007 Mean :0.034 Mean :0.055
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000 Max. :1.000
## Low.Price.Airline..LPA.US Low.Price.Airline..LPA.WN Low.Price.Airline..LPA.YX
## Min. :0.000 Min. :0.00 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000
## Mean :0.101 Mean :0.23 Mean :0.003
## 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000head(airdata)## Average.Flight.Fare Distance Average.Weekly.Passengers Market.Share.MLA
## 1 114.47 528 424.56 70.19
## 2 122.47 860 276.84 75.10
## 3 214.42 852 215.76 78.89
## 4 69.40 288 606.84 96.97
## 5 158.13 723 313.04 39.79
## 6 135.17 1204 199.02 40.68
## Average.MLA.Fare Market.Share.LPA Average.LPA.Fare City1ABQ City1ACY City1ALB
## 1 111.03 70.19 111.03 0 0 0
## 2 123.09 17.23 118.94 0 0 0
## 3 223.98 2.77 167.12 0 0 1
## 4 68.86 96.97 68.86 0 0 1
## 5 161.36 15.34 145.42 0 0 1
## 6 137.97 17.09 127.69 0 0 1
## City1AMA City1ATL City1AUS City1BDL City1BHM City1BNA City1BOI City1BOS
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1BTV City1BUF City1BUR City1BWI City1CAE City1CAK City1CHS City1CLE
## 1 0 0 0 0 0 1 0 0
## 2 0 0 0 0 0 1 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1CLT City1CMH City1COS City1CRP City1CVG City1DAY City1DEN City1DFW
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1DSM City1DTW City1ELP City1EUG City1FLL City1FNT City1GPT City1GRR
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1GSO City1GSP City1HRL City1IAD City1IAH City1IND City1ISP City1JAX
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1LAS City1LAX City1LGA City1LGB City1LIT City1MCI City1MCO City1MDT
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1MEM City1MHT City1MIA City1MKE City1MSN City1MSP City1MSY City1MYR
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1OAK City1OKC City1OMA City1ONT City1ORD City1ORF City1PDX City1PHF
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1PHL City1PHX City1PIT City1PSP City1PVD City1RDU City1RNO City1ROC
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1RSW City1SAN City1SAT City1SDF City1SEA City1SFO City1SJC City1SLC
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City1SMF City1SNA City1STL City1SYR City1TLH City1TPA City1TYS City2ATL
## 1 0 0 0 0 0 0 0 1
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 1
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2AUS City2BDL City2BHM City2BNA City2BOS City2BUF City2BWI City2CLE
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 1 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2CLT City2CMH City2CVG City2DAY City2DEN City2DFW City2DSM City2DTW
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2ELP City2FLL City2FNT City2GEG City2GPT City2GSO City2HPN City2HRL
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 1 0 0 0 0 0 0
## City2IAD City2IAH City2ICT City2IND City2ISP City2JAN City2JAX City2LAS
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2LAX City2LBB City2LGA City2LGB City2LIT City2MAF City2MCI City2MCO
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 1
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2MDT City2MEM City2MHT City2MIA City2MKE City2MSP City2MSY City2OAK
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2OKC City2OMA City2ONT City2ORD City2ORF City2PBI City2PDX City2PHF
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 1 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2PHL City2PHX City2PIT City2PNS City2PVD City2PWM City2RDU City2RIC
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2RNO City2ROC City2RSW City2SAN City2SAT City2SAV City2SDF City2SEA
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2SFO City2SJC City2SLC City2SMF City2SNA City2SRQ City2STL City2SYR
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## City2TLH City2TPA City2TUL City2TUS Market.Leading.Airline..MLA.AA
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Market.Leading.Airline..MLA.AS Market.Leading.Airline..MLA.B6
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Market.Leading.Airline..MLA.CO Market.Leading.Airline..MLA.DL
## 1 0 0
## 2 0 0
## 3 0 1
## 4 0 0
## 5 0 0
## 6 0 0
## Market.Leading.Airline..MLA.F9 Market.Leading.Airline..MLA.FL
## 1 0 1
## 2 0 1
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Market.Leading.Airline..MLA.G4 Market.Leading.Airline..MLA.HP
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Market.Leading.Airline..MLA.NK Market.Leading.Airline..MLA.NW
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Market.Leading.Airline..MLA.TZ Market.Leading.Airline..MLA.UA
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 1
## 6 0 0
## Market.Leading.Airline..MLA.US Market.Leading.Airline..MLA.WN
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 1
## 5 0 0
## 6 0 1
## Market.Leading.Airline..MLA.YX Low.Price.Airline..LPA.AA
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Low.Price.Airline..LPA.AQ Low.Price.Airline..LPA.AS Low.Price.Airline..LPA.AX
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Low.Price.Airline..LPA.B6 Low.Price.Airline..LPA.CO Low.Price.Airline..LPA.DL
## 1 0 0 0
## 2 0 0 1
## 3 0 1 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 1
## Low.Price.Airline..LPA.F9 Low.Price.Airline..LPA.FL Low.Price.Airline..LPA.G4
## 1 0 1 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Low.Price.Airline..LPA.HP Low.Price.Airline..LPA.NK Low.Price.Airline..LPA.NW
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Low.Price.Airline..LPA.SY Low.Price.Airline..LPA.TZ Low.Price.Airline..LPA.UA
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Low.Price.Airline..LPA.US Low.Price.Airline..LPA.WN Low.Price.Airline..LPA.YX
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 1 0
## 6 0 0 0tail(airdata)## Average.Flight.Fare Distance Average.Weekly.Passengers Market.Share.MLA
## 995 288.14 2329 787.50 46.51
## 996 136.16 1104 184.34 33.37
## 997 83.28 200 232.71 99.57
## 998 159.97 814 843.80 46.19
## 999 73.57 174 214.45 99.74
## 1000 126.67 859 475.65 56.28
## Average.MLA.Fare Market.Share.LPA Average.LPA.Fare City1ABQ City1ACY
## 995 329.20 30.43 252.34 0 0
## 996 135.82 28.65 118.51 0 0
## 997 82.55 99.57 82.55 0 0
## 998 159.65 13.89 159.02 0 0
## 999 73.44 99.74 73.44 0 0
## 1000 129.92 38.57 121.94 0 0
## City1ALB City1AMA City1ATL City1AUS City1BDL City1BHM City1BNA City1BOI
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1BOS City1BTV City1BUF City1BUR City1BWI City1CAE City1CAK City1CHS
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1CLE City1CLT City1CMH City1COS City1CRP City1CVG City1DAY City1DEN
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1DFW City1DSM City1DTW City1ELP City1EUG City1FLL City1FNT City1GPT
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1GRR City1GSO City1GSP City1HRL City1IAD City1IAH City1IND City1ISP
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 1 0 0 0
## City1JAX City1LAS City1LAX City1LGA City1LGB City1LIT City1MCI City1MCO
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1MDT City1MEM City1MHT City1MIA City1MKE City1MSN City1MSP City1MSY
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1MYR City1OAK City1OKC City1OMA City1ONT City1ORD City1ORF City1PDX
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1PHF City1PHL City1PHX City1PIT City1PSP City1PVD City1RDU City1RNO
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1ROC City1RSW City1SAN City1SAT City1SDF City1SEA City1SFO City1SJC
## 995 0 0 0 0 0 1 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City1SLC City1SMF City1SNA City1STL City1SYR City1TLH City1TPA City1TYS
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 1 0 0 0
## 997 0 0 0 0 0 1 0 0
## 998 0 0 0 0 0 0 1 0
## 999 0 0 0 0 0 0 1 0
## 1000 0 0 0 0 0 0 0 0
## City2ATL City2AUS City2BDL City2BHM City2BNA City2BOS City2BUF City2BWI
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2CLE City2CLT City2CMH City2CVG City2DAY City2DEN City2DFW City2DSM
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2DTW City2ELP City2FLL City2FNT City2GEG City2GPT City2GSO City2HPN
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2HRL City2IAD City2IAH City2ICT City2IND City2ISP City2JAN City2JAX
## 995 0 1 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 1 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2LAS City2LAX City2LBB City2LGA City2LGB City2LIT City2MAF City2MCI
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2MCO City2MDT City2MEM City2MHT City2MIA City2MKE City2MSP City2MSY
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2OAK City2OKC City2OMA City2ONT City2ORD City2ORF City2PBI City2PDX
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 1 0
## 1000 0 0 0 0 0 0 1 0
## City2PHF City2PHL City2PHX City2PIT City2PNS City2PVD City2PWM City2RDU
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2RIC City2RNO City2ROC City2RSW City2SAN City2SAT City2SAV City2SDF
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2SEA City2SFO City2SJC City2SLC City2SMF City2SNA City2SRQ City2STL
## 995 0 0 0 0 0 0 0 0
## 996 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0
## 999 0 0 0 0 0 0 0 0
## 1000 0 0 0 0 0 0 0 0
## City2SYR City2TLH City2TPA City2TUL City2TUS
## 995 0 0 0 0 0
## 996 0 0 1 0 0
## 997 0 0 1 0 0
## 998 0 0 0 0 0
## 999 0 0 0 0 0
## 1000 0 0 0 0 0
## Market.Leading.Airline..MLA.AA Market.Leading.Airline..MLA.AS
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Market.Leading.Airline..MLA.B6 Market.Leading.Airline..MLA.CO
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Market.Leading.Airline..MLA.DL Market.Leading.Airline..MLA.F9
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Market.Leading.Airline..MLA.FL Market.Leading.Airline..MLA.G4
## 995 0 0
## 996 0 0
## 997 1 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Market.Leading.Airline..MLA.HP Market.Leading.Airline..MLA.NK
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Market.Leading.Airline..MLA.NW Market.Leading.Airline..MLA.TZ
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Market.Leading.Airline..MLA.UA Market.Leading.Airline..MLA.US
## 995 1 0
## 996 0 1
## 997 0 0
## 998 0 1
## 999 0 0
## 1000 0 1
## Market.Leading.Airline..MLA.WN Market.Leading.Airline..MLA.YX
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 1 0
## 1000 0 0
## Low.Price.Airline..LPA.AA Low.Price.Airline..LPA.AQ
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Low.Price.Airline..LPA.AS Low.Price.Airline..LPA.AX
## 995 1 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Low.Price.Airline..LPA.B6 Low.Price.Airline..LPA.CO
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Low.Price.Airline..LPA.DL Low.Price.Airline..LPA.F9
## 995 0 0
## 996 1 0
## 997 0 0
## 998 1 0
## 999 0 0
## 1000 1 0
## Low.Price.Airline..LPA.FL Low.Price.Airline..LPA.G4
## 995 0 0
## 996 0 0
## 997 1 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Low.Price.Airline..LPA.HP Low.Price.Airline..LPA.NK
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Low.Price.Airline..LPA.NW Low.Price.Airline..LPA.SY
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Low.Price.Airline..LPA.TZ Low.Price.Airline..LPA.UA
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 0
## 1000 0 0
## Low.Price.Airline..LPA.US Low.Price.Airline..LPA.WN
## 995 0 0
## 996 0 0
## 997 0 0
## 998 0 0
## 999 0 1
## 1000 0 0
## Low.Price.Airline..LPA.YX
## 995 0
## 996 0
## 997 0
## 998 0
## 999 0
## 1000 0#missing value analysis
library(DataExplorer)
is.na(airdata)## Average.Flight.Fare Distance Average.Weekly.Passengers Market.Share.MLA
## 1 FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE
## 5 FALSE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE FALSE
## 7 FALSE FALSE FALSE FALSE
## 8 FALSE FALSE FALSE FALSE
## 9 FALSE FALSE FALSE FALSE
## 10 FALSE FALSE FALSE FALSE
## 11 FALSE FALSE FALSE FALSE
## 12 FALSE FALSE FALSE FALSE
## 13 FALSE FALSE FALSE FALSE
## 14 FALSE FALSE FALSE FALSE
## 15 FALSE FALSE FALSE FALSE
## 16 FALSE FALSE FALSE FALSE
## 17 FALSE FALSE FALSE FALSE
## 18 FALSE FALSE FALSE FALSE
## 19 FALSE FALSE FALSE FALSE
## 20 FALSE FALSE FALSE FALSE
## 21 FALSE FALSE FALSE FALSE
## 22 FALSE FALSE FALSE FALSE
## 23 FALSE FALSE FALSE FALSE
## 24 FALSE FALSE FALSE FALSE
## 25 FALSE FALSE FALSE FALSE
## 26 FALSE FALSE FALSE FALSE
## 27 FALSE FALSE FALSE FALSE
## 28 FALSE FALSE FALSE FALSE
## 29 FALSE FALSE FALSE FALSE
## 30 FALSE FALSE FALSE FALSE
## 31 FALSE FALSE FALSE FALSE
## 32 FALSE FALSE FALSE FALSE
## 33 FALSE FALSE FALSE FALSE
## 34 FALSE FALSE FALSE FALSE
## 35 FALSE FALSE FALSE FALSE
## 36 FALSE FALSE FALSE FALSE
## 37 FALSE FALSE FALSE FALSE
## 38 FALSE FALSE FALSE FALSE
## 39 FALSE FALSE FALSE FALSE
## 40 FALSE FALSE FALSE FALSE
## 41 FALSE FALSE FALSE FALSE
## 42 FALSE FALSE FALSE FALSE
## 43 FALSE FALSE FALSE FALSE
## 44 FALSE FALSE FALSE FALSE
## 45 FALSE FALSE FALSE FALSE
## 46 FALSE FALSE FALSE FALSE
## 47 FALSE FALSE FALSE FALSE
## 48 FALSE FALSE FALSE FALSE
## 49 FALSE FALSE FALSE FALSE
## 50 FALSE FALSE FALSE FALSE
## 51 FALSE FALSE FALSE FALSE
## 52 FALSE FALSE FALSE FALSE
## 53 FALSE FALSE FALSE FALSE
## 54 FALSE FALSE FALSE FALSE
## 55 FALSE FALSE FALSE FALSE
## 56 FALSE FALSE FALSE FALSE
## 57 FALSE FALSE FALSE FALSE
## 58 FALSE FALSE FALSE FALSE
## 59 FALSE FALSE FALSE FALSE
## 60 FALSE FALSE FALSE FALSE
## 61 FALSE FALSE FALSE FALSE
## 62 FALSE FALSE FALSE FALSE
## 63 FALSE FALSE FALSE FALSE
## 64 FALSE FALSE FALSE FALSE
## 65 FALSE FALSE FALSE FALSE
## 66 FALSE FALSE FALSE FALSE
## 67 FALSE FALSE FALSE FALSE
## 68 FALSE FALSE FALSE FALSE
## 69 FALSE FALSE FALSE FALSE
## 70 FALSE FALSE FALSE FALSE
## 71 FALSE FALSE FALSE FALSE
## 72 FALSE FALSE FALSE FALSE
## 73 FALSE FALSE FALSE FALSE
## 74 FALSE FALSE FALSE FALSE
## 75 FALSE FALSE FALSE FALSE
## 76 FALSE FALSE FALSE FALSE
## 77 FALSE FALSE FALSE FALSE
## 78 FALSE FALSE FALSE FALSE
## 79 FALSE FALSE FALSE FALSE
## 80 FALSE FALSE FALSE FALSE
## 81 FALSE FALSE FALSE FALSE
## 82 FALSE FALSE FALSE FALSE
## 83 FALSE FALSE FALSE FALSE
## 84 FALSE FALSE FALSE FALSE
## 85 FALSE FALSE FALSE FALSE
## 86 FALSE FALSE FALSE FALSE
## 87 FALSE FALSE FALSE FALSE
## 88 FALSE FALSE FALSE FALSE
## 89 FALSE FALSE FALSE FALSE
## 90 FALSE FALSE FALSE FALSE
## 91 FALSE FALSE FALSE FALSE
## 92 FALSE FALSE FALSE FALSE
## 93 FALSE FALSE FALSE FALSE
## 94 FALSE FALSE FALSE FALSE
## 95 FALSE FALSE FALSE FALSE
## 96 FALSE FALSE FALSE FALSE
## 97 FALSE FALSE FALSE FALSE
## 98 FALSE FALSE FALSE FALSE
## 99 FALSE FALSE FALSE FALSE
## 100 FALSE FALSE FALSE FALSE
## 101 FALSE FALSE FALSE FALSE
## 102 FALSE FALSE FALSE FALSE
## 103 FALSE FALSE FALSE FALSE
## 104 FALSE FALSE FALSE FALSE
## 105 FALSE FALSE FALSE FALSE
## 106 FALSE FALSE FALSE FALSE
## 107 FALSE FALSE FALSE FALSE
## 108 FALSE FALSE FALSE FALSE
## 109 FALSE FALSE FALSE FALSE
## 110 FALSE FALSE FALSE FALSE
## 111 FALSE FALSE FALSE FALSE
## 112 FALSE FALSE FALSE FALSE
## 113 FALSE FALSE FALSE FALSE
## 114 FALSE FALSE FALSE FALSE
## 115 FALSE FALSE FALSE FALSE
## 116 FALSE FALSE FALSE FALSE
## 117 FALSE FALSE FALSE FALSE
## 118 FALSE FALSE FALSE FALSE
## 119 FALSE FALSE FALSE FALSE
## 120 FALSE FALSE FALSE FALSE
## 121 FALSE FALSE FALSE FALSE
## 122 FALSE FALSE FALSE FALSE
## 123 FALSE FALSE FALSE FALSE
## 124 FALSE FALSE FALSE FALSE
## 125 FALSE FALSE FALSE FALSE
## 126 FALSE FALSE FALSE FALSE
## 127 FALSE FALSE FALSE FALSE
## 128 FALSE FALSE FALSE FALSE
## 129 FALSE FALSE FALSE FALSE
## 130 FALSE FALSE FALSE FALSE
## 131 FALSE FALSE FALSE FALSE
## 132 FALSE FALSE FALSE FALSE
## 133 FALSE FALSE FALSE FALSE
## 134 FALSE FALSE FALSE FALSE
## 135 FALSE FALSE FALSE FALSE
## 136 FALSE FALSE FALSE FALSE
## 137 FALSE FALSE FALSE FALSE
## 138 FALSE FALSE FALSE FALSE
## 139 FALSE FALSE FALSE FALSE
## 140 FALSE FALSE FALSE FALSE
## 141 FALSE FALSE FALSE FALSE
## 142 FALSE FALSE FALSE FALSE
## 143 FALSE FALSE FALSE FALSE
## 144 FALSE FALSE FALSE FALSE
## 145 FALSE FALSE FALSE FALSE
## 146 FALSE FALSE FALSE FALSE
## 147 FALSE FALSE FALSE FALSE
## 148 FALSE FALSE FALSE FALSE
## 149 FALSE FALSE FALSE FALSE
## 150 FALSE FALSE FALSE FALSE
## 151 FALSE FALSE FALSE FALSE
## 152 FALSE FALSE FALSE FALSE
## 153 FALSE FALSE FALSE FALSE
## 154 FALSE FALSE FALSE FALSE
## 155 FALSE FALSE FALSE FALSE
## 156 FALSE FALSE FALSE FALSE
## 157 FALSE FALSE FALSE FALSE
## 158 FALSE FALSE FALSE FALSE
## 159 FALSE FALSE FALSE FALSE
## 160 FALSE FALSE FALSE FALSE
## 161 FALSE FALSE FALSE FALSE
## 162 FALSE FALSE FALSE FALSE
## 163 FALSE FALSE FALSE FALSE
## 164 FALSE FALSE FALSE FALSE
## 165 FALSE FALSE FALSE FALSE
## 166 FALSE FALSE FALSE FALSE
## 167 FALSE FALSE FALSE FALSE
## 168 FALSE FALSE FALSE FALSE
## 169 FALSE FALSE FALSE FALSE
## 170 FALSE FALSE FALSE FALSE
## 171 FALSE FALSE FALSE FALSE
## 172 FALSE FALSE FALSE FALSE
## 173 FALSE FALSE FALSE FALSE
## 174 FALSE FALSE FALSE FALSE
## 175 FALSE FALSE FALSE FALSE
## 176 FALSE FALSE FALSE FALSE
## 177 FALSE FALSE FALSE FALSE
## 178 FALSE FALSE FALSE FALSE
## 179 FALSE FALSE FALSE FALSE
## 180 FALSE FALSE FALSE FALSE
## 181 FALSE FALSE FALSE FALSE
## 182 FALSE FALSE FALSE FALSE
## 183 FALSE FALSE FALSE FALSE
## 184 FALSE FALSE FALSE FALSE
## 185 FALSE FALSE FALSE FALSE
## 186 FALSE FALSE FALSE FALSE
## 187 FALSE FALSE FALSE FALSE
## 188 FALSE FALSE FALSE FALSE
## 189 FALSE FALSE FALSE FALSE
## 190 FALSE FALSE FALSE FALSE
## 191 FALSE FALSE FALSE FALSE
## 192 FALSE FALSE FALSE FALSE
## 193 FALSE FALSE FALSE FALSE
## 194 FALSE FALSE FALSE FALSE
## 195 FALSE FALSE FALSE FALSE
## 196 FALSE FALSE FALSE FALSE
## 197 FALSE FALSE FALSE FALSE
## 198 FALSE FALSE FALSE FALSE
## 199 FALSE FALSE FALSE FALSE
## 200 FALSE FALSE FALSE FALSE
## 201 FALSE FALSE FALSE FALSE
## 202 FALSE FALSE FALSE FALSE
## 203 FALSE FALSE FALSE FALSE
## 204 FALSE FALSE FALSE FALSE
## 205 FALSE FALSE FALSE FALSE
## 206 FALSE FALSE FALSE FALSE
## 207 FALSE FALSE FALSE FALSE
## 208 FALSE FALSE FALSE FALSE
## 209 FALSE FALSE FALSE FALSE
## 210 FALSE FALSE FALSE FALSE
## 211 FALSE FALSE FALSE FALSE
## 212 FALSE FALSE FALSE FALSE
## 213 FALSE FALSE FALSE FALSE
## 214 FALSE FALSE FALSE FALSE
## 215 FALSE FALSE FALSE FALSE
## 216 FALSE FALSE FALSE FALSE
## 217 FALSE FALSE FALSE FALSE
## 218 FALSE FALSE FALSE FALSE
## 219 FALSE FALSE FALSE FALSE
## 220 FALSE FALSE FALSE FALSE
## 221 FALSE FALSE FALSE FALSE
## 222 FALSE FALSE FALSE FALSE
## 223 FALSE FALSE FALSE FALSE
## 224 FALSE FALSE FALSE FALSE
## 225 FALSE FALSE FALSE FALSE
## 226 FALSE FALSE FALSE FALSE
## 227 FALSE FALSE FALSE FALSE
## 228 FALSE FALSE FALSE FALSE
## 229 FALSE FALSE FALSE FALSE
## 230 FALSE FALSE FALSE FALSE
## 231 FALSE FALSE FALSE FALSE
## 232 FALSE FALSE FALSE FALSE
## 233 FALSE FALSE FALSE FALSE
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## City1ALB City1AMA City1ATL City1AUS City1BDL City1BHM City1BNA City1BOI
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## City2ATL City2AUS City2BDL City2BHM City2BNA City2BOS City2BUF City2BWI
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## City2CLE City2CLT City2CMH City2CVG City2DAY City2DEN City2DFW City2DSM
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## City2HRL City2IAD City2IAH City2ICT City2IND City2ISP City2JAN City2JAX
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## 459 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 460 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## City2LAS City2LAX City2LBB City2LGA City2LGB City2LIT City2MAF City2MCI
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## City2OAK City2OKC City2OMA City2ONT City2ORD City2ORF City2PBI City2PDX
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## 459 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 460 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## City2PHF City2PHL City2PHX City2PIT City2PNS City2PVD City2PWM City2RDU
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 7 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## City2RIC City2RNO City2ROC City2RSW City2SAN City2SAT City2SAV City2SDF
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## Market.Leading.Airline..MLA.FL Market.Leading.Airline..MLA.G4
## 1 FALSE FALSE
## 2 FALSE FALSE
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## 312 FALSE FALSE
## 313 FALSE FALSE
## 314 FALSE FALSE
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## 392 FALSE FALSE
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## 452 FALSE FALSE
## 453 FALSE FALSE
## 454 FALSE FALSE
## 455 FALSE FALSE
## 456 FALSE FALSE
## 457 FALSE FALSE
## 458 FALSE FALSE
## 459 FALSE FALSE
## 460 FALSE FALSE
## Market.Leading.Airline..MLA.HP Market.Leading.Airline..MLA.NK
## 1 FALSE FALSE
## 2 FALSE FALSE
## 3 FALSE FALSE
## 4 FALSE FALSE
## 5 FALSE FALSE
## 6 FALSE FALSE
## 7 FALSE FALSE
## 8 FALSE FALSE
## 9 FALSE FALSE
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## 12 FALSE FALSE
## 13 FALSE FALSE
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## 100 FALSE FALSE
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## 102 FALSE FALSE
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## 104 FALSE FALSE
## 105 FALSE FALSE
## 106 FALSE FALSE
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## 110 FALSE FALSE
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## 112 FALSE FALSE
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## 115 FALSE FALSE
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## 119 FALSE FALSE
## 120 FALSE FALSE
## 121 FALSE FALSE
## 122 FALSE FALSE
## 123 FALSE FALSE
## 124 FALSE FALSE
## 125 FALSE FALSE
## 126 FALSE FALSE
## 127 FALSE FALSE
## 128 FALSE FALSE
## 129 FALSE FALSE
## 130 FALSE FALSE
## 131 FALSE FALSE
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## Market.Leading.Airline..MLA.UA Market.Leading.Airline..MLA.US
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## Market.Leading.Airline..MLA.WN Market.Leading.Airline..MLA.YX
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## Low.Price.Airline..LPA.AS Low.Price.Airline..LPA.AX
## 1 FALSE FALSE
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## Low.Price.Airline..LPA.B6 Low.Price.Airline..LPA.CO
## 1 FALSE FALSE
## 2 FALSE FALSE
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## Low.Price.Airline..LPA.DL Low.Price.Airline..LPA.F9
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## 459 FALSE FALSE
## 460 FALSE FALSE
## Low.Price.Airline..LPA.FL Low.Price.Airline..LPA.G4
## 1 FALSE FALSE
## 2 FALSE FALSE
## 3 FALSE FALSE
## 4 FALSE FALSE
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## 454 FALSE FALSE
## 455 FALSE FALSE
## 456 FALSE FALSE
## 457 FALSE FALSE
## 458 FALSE FALSE
## 459 FALSE FALSE
## 460 FALSE FALSE
## Low.Price.Airline..LPA.HP Low.Price.Airline..LPA.NK
## 1 FALSE FALSE
## 2 FALSE FALSE
## 3 FALSE FALSE
## 4 FALSE FALSE
## 5 FALSE FALSE
## 6 FALSE FALSE
## 7 FALSE FALSE
## 8 FALSE FALSE
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## 32 FALSE FALSE
## 33 FALSE FALSE
## 34 FALSE FALSE
## 35 FALSE FALSE
## 36 FALSE FALSE
## 37 FALSE FALSE
## 38 FALSE FALSE
## 39 FALSE FALSE
## 40 FALSE FALSE
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## Low.Price.Airline..LPA.NW Low.Price.Airline..LPA.SY
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## 459 FALSE FALSE
## 460 FALSE FALSE
## Low.Price.Airline..LPA.TZ Low.Price.Airline..LPA.UA
## 1 FALSE FALSE
## 2 FALSE FALSE
## 3 FALSE FALSE
## 4 FALSE FALSE
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## 450 FALSE FALSE
## 451 FALSE FALSE
## 452 FALSE FALSE
## 453 FALSE FALSE
## 454 FALSE FALSE
## 455 FALSE FALSE
## 456 FALSE FALSE
## 457 FALSE FALSE
## 458 FALSE FALSE
## 459 FALSE FALSE
## 460 FALSE FALSE
## Low.Price.Airline..LPA.US Low.Price.Airline..LPA.WN
## 1 FALSE FALSE
## 2 FALSE FALSE
## 3 FALSE FALSE
## 4 FALSE FALSE
## 5 FALSE FALSE
## 6 FALSE FALSE
## 7 FALSE FALSE
## 8 FALSE FALSE
## 9 FALSE FALSE
## 10 FALSE FALSE
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## Low.Price.Airline..LPA.YX
## 1 FALSE
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## 380 FALSE
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## 383 FALSE
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## 385 FALSE
## 386 FALSE
## 387 FALSE
## 388 FALSE
## 389 FALSE
## 390 FALSE
## 391 FALSE
## 392 FALSE
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## 396 FALSE
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## 399 FALSE
## 400 FALSE
## 401 FALSE
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## 411 FALSE
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## 436 FALSE
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## 460 FALSE
## [ reached getOption("max.print") -- omitted 540 rows ]plot_missing(airdata)
#The dataset has no missing values.
#This ensures reliability and accurate in analyses done in this project.
#Hence, variable imputation and MCAR test are not required.#Standardize all variables
air1<-as.data.frame(scale(airdata))
air<-as.data.frame(scale(airline_clean))
# Printing the resulting data frame to check
print(head(air))## Average.Flight.Fare Distance Average.Weekly.Passengers Market.Share.MLA
## 1 -0.9368159 -0.8505674 -0.2338755 0.5154121
## 2 -0.7897959 -0.3327826 -0.6833202 0.7708175
## 3 0.9000153 -0.3452593 -0.8691588 0.9679634
## 4 -1.7650899 -1.2248697 0.3207194 1.9084378
## 5 -0.1344543 -0.5464468 -0.5731801 -1.0659165
## 6 -0.5564017 0.2037174 -0.9200910 -1.0196210
## Average.MLA.Fare Market.Share.LPA Average.LPA.Fare
## 1 -0.9379300 1.3351633 -0.735564651
## 2 -0.7417904 -0.6291264 -0.565193579
## 3 0.8990496 -1.1654487 0.472540712
## 4 -1.6237683 2.3284352 -1.643851390
## 5 -0.1193803 -0.6992266 0.005151045
## 6 -0.4997872 -0.6343190 -0.376730004print(head(air))## Average.Flight.Fare Distance Average.Weekly.Passengers Market.Share.MLA
## 1 -0.9368159 -0.8505674 -0.2338755 0.5154121
## 2 -0.7897959 -0.3327826 -0.6833202 0.7708175
## 3 0.9000153 -0.3452593 -0.8691588 0.9679634
## 4 -1.7650899 -1.2248697 0.3207194 1.9084378
## 5 -0.1344543 -0.5464468 -0.5731801 -1.0659165
## 6 -0.5564017 0.2037174 -0.9200910 -1.0196210
## Average.MLA.Fare Market.Share.LPA Average.LPA.Fare
## 1 -0.9379300 1.3351633 -0.735564651
## 2 -0.7417904 -0.6291264 -0.565193579
## 3 0.8990496 -1.1654487 0.472540712
## 4 -1.6237683 2.3284352 -1.643851390
## 5 -0.1193803 -0.6992266 0.005151045
## 6 -0.4997872 -0.6343190 -0.376730004summary(air)## Average.Flight.Fare Distance Average.Weekly.Passengers
## Min. :-2.11206 Min. :-1.5056 Min. :-0.9737
## 1st Qu.:-0.68449 1st Qu.:-0.7679 1st Qu.:-0.7667
## Median :-0.05488 Median :-0.2236 Median :-0.3837
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.63262 3rd Qu.: 0.6451 3rd Qu.: 0.4596
## Max. : 4.33312 Max. : 2.5743 Max. : 3.0987
## Market.Share.MLA Average.MLA.Fare Market.Share.LPA Average.LPA.Fare
## Min. :-2.21602 Min. :-1.92204 Min. :-1.2289 Min. :-2.0389
## 1st Qu.:-0.71064 1st Qu.:-0.68503 1st Qu.:-0.7886 1st Qu.:-0.6126
## Median :-0.03961 Median :-0.09759 Median :-0.2946 Median :-0.1124
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.70944 3rd Qu.: 0.60045 3rd Qu.: 0.6709 3rd Qu.: 0.5468
## Max. : 2.06605 Max. : 5.22600 Max. : 2.4408 Max. : 5.2287summary(air1)## Average.Flight.Fare Distance Average.Weekly.Passengers
## Min. :-2.03837 Min. :-1.4754 Min. :-0.6404
## 1st Qu.:-0.67552 1st Qu.:-0.7828 1st Qu.:-0.5415
## Median :-0.03667 Median :-0.2145 Median :-0.3490
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.61441 3rd Qu.: 0.6149 3rd Qu.: 0.1274
## Max. : 4.29608 Max. : 2.5918 Max. :10.8001
## Market.Share.MLA Average.MLA.Fare Market.Share.LPA Average.LPA.Fare
## Min. :-2.19171 Min. :-1.85329 Min. :-1.2360 Min. :-1.9815
## 1st Qu.:-0.70316 1st Qu.:-0.67926 1st Qu.:-0.7881 1st Qu.:-0.6230
## Median :-0.04588 Median :-0.08375 Median :-0.3104 Median :-0.1137
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.69091 3rd Qu.: 0.57979 3rd Qu.: 0.6798 3rd Qu.: 0.5365
## Max. : 2.05865 Max. : 5.16090 Max. : 2.4153 Max. : 5.1821
## City1ABQ City1ACY City1ALB City1AMA
## Min. :-0.1353 Min. :-0.06334 Min. :-0.08976 Min. :-0.04474
## 1st Qu.:-0.1353 1st Qu.:-0.06334 1st Qu.:-0.08976 1st Qu.:-0.04474
## Median :-0.1353 Median :-0.06334 Median :-0.08976 Median :-0.04474
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.1353 3rd Qu.:-0.06334 3rd Qu.:-0.08976 3rd Qu.:-0.04474
## Max. : 7.3825 Max. :15.77184 Max. :11.12996 Max. :22.32714
## City1ATL City1AUS City1BDL City1BHM
## Min. :-0.2614 Min. :-0.1464 Min. :-0.1191 Min. :-0.08976
## 1st Qu.:-0.2614 1st Qu.:-0.1464 1st Qu.:-0.1191 1st Qu.:-0.08976
## Median :-0.2614 Median :-0.1464 Median :-0.1191 Median :-0.08976
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.:-0.2614 3rd Qu.:-0.1464 3rd Qu.:-0.1191 3rd Qu.:-0.08976
## Max. : 3.8224 Max. : 6.8244 Max. : 8.3880 Max. :11.12996
## City1BNA City1BOI City1BOS City1BTV
## Min. :-0.1054 Min. :-0.08976 Min. :-0.1986 Min. :-0.03162
## 1st Qu.:-0.1054 1st Qu.:-0.08976 1st Qu.:-0.1986 1st Qu.:-0.03162
## Median :-0.1054 Median :-0.08976 Median :-0.1986 Median :-0.03162
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.:-0.1054 3rd Qu.:-0.08976 3rd Qu.:-0.1986 3rd Qu.:-0.03162
## Max. : 9.4773 Max. :11.12996 Max. : 5.0290 Max. :31.59115
## City1BUF City1BUR City1BWI City1CAE
## Min. :-0.09525 Min. :-0.1005 Min. :-0.222 Min. :-0.03162
## 1st Qu.:-0.09525 1st Qu.:-0.1005 1st Qu.:-0.222 1st Qu.:-0.03162
## Median :-0.09525 Median :-0.1005 Median :-0.222 Median :-0.03162
## Mean : 0.00000 Mean : 0.0000 Mean : 0.000 Mean : 0.00000
## 3rd Qu.:-0.09525 3rd Qu.:-0.1005 3rd Qu.:-0.222 3rd Qu.:-0.03162
## Max. :10.48814 Max. : 9.9449 Max. : 4.501 Max. :31.59115
## City1CAK City1CHS City1CLE City1CLT
## Min. :-0.04474 Min. :-0.03162 Min. :-0.1499 Min. :-0.1353
## 1st Qu.:-0.04474 1st Qu.:-0.03162 1st Qu.:-0.1499 1st Qu.:-0.1353
## Median :-0.04474 Median :-0.03162 Median :-0.1499 Median :-0.1353
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.04474 3rd Qu.:-0.03162 3rd Qu.:-0.1499 3rd Qu.:-0.1353
## Max. :22.32714 Max. :31.59115 Max. : 6.6641 Max. : 7.3825
## City1CMH City1COS City1CRP City1CVG
## Min. :-0.1353 Min. :-0.05483 Min. :-0.04474 Min. :-0.1102
## 1st Qu.:-0.1353 1st Qu.:-0.05483 1st Qu.:-0.04474 1st Qu.:-0.1102
## Median :-0.1353 Median :-0.05483 Median :-0.04474 Median :-0.1102
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.1353 3rd Qu.:-0.05483 3rd Qu.:-0.04474 3rd Qu.:-0.1102
## Max. : 7.3825 Max. :18.22089 Max. :22.32714 Max. : 9.0692
## City1DAY City1DEN City1DFW City1DSM
## Min. :-0.05483 Min. :-0.1931 Min. :-0.2341 Min. :-0.04474
## 1st Qu.:-0.05483 1st Qu.:-0.1931 1st Qu.:-0.2341 1st Qu.:-0.04474
## Median :-0.05483 Median :-0.1931 Median :-0.2341 Median :-0.04474
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.:-0.05483 3rd Qu.:-0.1931 3rd Qu.:-0.2341 3rd Qu.:-0.04474
## Max. :18.22089 Max. : 5.1721 Max. : 4.2676 Max. :22.32714
## City1DTW City1ELP City1EUG City1FLL
## Min. :-0.1846 Min. :-0.07765 Min. :-0.03162 Min. :-0.1727
## 1st Qu.:-0.1846 1st Qu.:-0.07765 1st Qu.:-0.03162 1st Qu.:-0.1727
## Median :-0.1846 Median :-0.07765 Median :-0.03162 Median :-0.1727
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.1846 3rd Qu.:-0.07765 3rd Qu.:-0.03162 3rd Qu.:-0.1727
## Max. : 5.4105 Max. :12.86472 Max. :31.59115 Max. : 5.7835
## City1FNT City1GPT City1GRR City1GSO
## Min. :-0.03162 Min. :-0.03162 Min. :-0.06334 Min. :-0.05483
## 1st Qu.:-0.03162 1st Qu.:-0.03162 1st Qu.:-0.06334 1st Qu.:-0.05483
## Median :-0.03162 Median :-0.03162 Median :-0.06334 Median :-0.05483
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.03162 3rd Qu.:-0.03162 3rd Qu.:-0.06334 3rd Qu.:-0.05483
## Max. :31.59115 Max. :31.59115 Max. :15.77184 Max. :18.22089
## City1GSP City1HRL City1IAD City1IAH
## Min. :-0.03162 Min. :-0.03162 Min. :-0.03162 Min. :-0.1986
## 1st Qu.:-0.03162 1st Qu.:-0.03162 1st Qu.:-0.03162 1st Qu.:-0.1986
## Median :-0.03162 Median :-0.03162 Median :-0.03162 Median :-0.1986
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.03162 3rd Qu.:-0.03162 3rd Qu.:-0.03162 3rd Qu.:-0.1986
## Max. :31.59115 Max. :31.59115 Max. :31.59115 Max. : 5.0290
## City1IND City1ISP City1JAX City1LAS
## Min. :-0.1102 Min. :-0.07085 Min. :-0.08392 Min. :-0.1959
## 1st Qu.:-0.1102 1st Qu.:-0.07085 1st Qu.:-0.08392 1st Qu.:-0.1959
## Median :-0.1102 Median :-0.07085 Median :-0.08392 Median :-0.1959
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.1102 3rd Qu.:-0.07085 3rd Qu.:-0.08392 3rd Qu.:-0.1959
## Max. : 9.0692 Max. :14.09968 Max. :11.90442 Max. : 5.0991
## City1LAX City1LGA City1LGB City1LIT
## Min. :-0.1788 Min. :-0.1696 Min. :-0.06334 Min. :-0.03162
## 1st Qu.:-0.1788 1st Qu.:-0.1696 1st Qu.:-0.06334 1st Qu.:-0.03162
## Median :-0.1788 Median :-0.1696 Median :-0.06334 Median :-0.03162
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.1788 3rd Qu.:-0.1696 3rd Qu.:-0.06334 3rd Qu.:-0.03162
## Max. : 5.5881 Max. : 5.8889 Max. :15.77184 Max. :31.59115
## City1MCI City1MCO City1MDT City1MEM
## Min. :-0.1534 Min. :-0.1314 Min. :-0.03162 Min. :-0.07765
## 1st Qu.:-0.1534 1st Qu.:-0.1314 1st Qu.:-0.03162 1st Qu.:-0.07765
## Median :-0.1534 Median :-0.1314 Median :-0.03162 Median :-0.07765
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.1534 3rd Qu.:-0.1314 3rd Qu.:-0.03162 3rd Qu.:-0.07765
## Max. : 6.5143 Max. : 7.6004 Max. :31.59115 Max. :12.86472
## City1MHT City1MIA City1MKE City1MSN
## Min. :-0.05483 Min. :-0.1147 Min. :-0.08392 Min. :-0.03162
## 1st Qu.:-0.05483 1st Qu.:-0.1147 1st Qu.:-0.08392 1st Qu.:-0.03162
## Median :-0.05483 Median :-0.1147 Median :-0.08392 Median :-0.03162
## Mean : 0.00000 Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.05483 3rd Qu.:-0.1147 3rd Qu.:-0.08392 3rd Qu.:-0.03162
## Max. :18.22089 Max. : 8.7090 Max. :11.90442 Max. :31.59115
## City1MSP City1MSY City1MYR City1OAK
## Min. :-0.1428 Min. :-0.1233 Min. :-0.03162 Min. :-0.1102
## 1st Qu.:-0.1428 1st Qu.:-0.1233 1st Qu.:-0.03162 1st Qu.:-0.1102
## Median :-0.1428 Median :-0.1233 Median :-0.03162 Median :-0.1102
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.1428 3rd Qu.:-0.1233 3rd Qu.:-0.03162 3rd Qu.:-0.1102
## Max. : 6.9965 Max. : 8.0994 Max. :31.59115 Max. : 9.0692
## City1OKC City1OMA City1ONT City1ORD
## Min. :-0.04474 Min. :-0.06334 Min. :-0.08976 Min. :-0.2592
## 1st Qu.:-0.04474 1st Qu.:-0.06334 1st Qu.:-0.08976 1st Qu.:-0.2592
## Median :-0.04474 Median :-0.06334 Median :-0.08976 Median :-0.2592
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.04474 3rd Qu.:-0.06334 3rd Qu.:-0.08976 3rd Qu.:-0.2592
## Max. :22.32714 Max. :15.77184 Max. :11.12996 Max. : 3.8546
## City1ORF City1PDX City1PHF City1PHL
## Min. :-0.05483 Min. :-0.1054 Min. :-0.03162 Min. :-0.1054
## 1st Qu.:-0.05483 1st Qu.:-0.1054 1st Qu.:-0.03162 1st Qu.:-0.1054
## Median :-0.05483 Median :-0.1054 Median :-0.03162 Median :-0.1054
## Mean : 0.00000 Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.05483 3rd Qu.:-0.1054 3rd Qu.:-0.03162 3rd Qu.:-0.1054
## Max. :18.22089 Max. : 9.4773 Max. :31.59115 Max. : 9.4773
## City1PHX City1PIT City1PSP City1PVD
## Min. :-0.1353 Min. :-0.05483 Min. :-0.05483 Min. :-0.07085
## 1st Qu.:-0.1353 1st Qu.:-0.05483 1st Qu.:-0.05483 1st Qu.:-0.07085
## Median :-0.1353 Median :-0.05483 Median :-0.05483 Median :-0.07085
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.1353 3rd Qu.:-0.05483 3rd Qu.:-0.05483 3rd Qu.:-0.07085
## Max. : 7.3825 Max. :18.22089 Max. :18.22089 Max. :14.09968
## City1RDU City1RNO City1ROC City1RSW
## Min. :-0.07085 Min. :-0.05483 Min. :-0.03162 Min. :-0.08976
## 1st Qu.:-0.07085 1st Qu.:-0.05483 1st Qu.:-0.03162 1st Qu.:-0.08976
## Median :-0.07085 Median :-0.05483 Median :-0.03162 Median :-0.08976
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.07085 3rd Qu.:-0.05483 3rd Qu.:-0.03162 3rd Qu.:-0.08976
## Max. :14.09968 Max. :18.22089 Max. :31.59115 Max. :11.12996
## City1SAN City1SAT City1SDF City1SEA
## Min. :-0.07765 Min. :-0.05483 Min. :-0.06334 Min. :-0.06334
## 1st Qu.:-0.07765 1st Qu.:-0.05483 1st Qu.:-0.06334 1st Qu.:-0.06334
## Median :-0.07765 Median :-0.05483 Median :-0.06334 Median :-0.06334
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.07765 3rd Qu.:-0.05483 3rd Qu.:-0.06334 3rd Qu.:-0.06334
## Max. :12.86472 Max. :18.22089 Max. :15.77184 Max. :15.77184
## City1SFO City1SJC City1SLC City1SMF
## Min. :-0.06334 Min. :-0.06334 Min. :-0.08392 Min. :-0.07765
## 1st Qu.:-0.06334 1st Qu.:-0.06334 1st Qu.:-0.08392 1st Qu.:-0.07765
## Median :-0.06334 Median :-0.06334 Median :-0.08392 Median :-0.07765
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.06334 3rd Qu.:-0.06334 3rd Qu.:-0.08392 3rd Qu.:-0.07765
## Max. :15.77184 Max. :15.77184 Max. :11.90442 Max. :12.86472
## City1SNA City1STL City1SYR City1TLH
## Min. :-0.03162 Min. :-0.09525 Min. :-0.03162 Min. :-0.03162
## 1st Qu.:-0.03162 1st Qu.:-0.09525 1st Qu.:-0.03162 1st Qu.:-0.03162
## Median :-0.03162 Median :-0.09525 Median :-0.03162 Median :-0.03162
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.03162 3rd Qu.:-0.09525 3rd Qu.:-0.03162 3rd Qu.:-0.03162
## Max. :31.59115 Max. :10.48814 Max. :31.59115 Max. :31.59115
## City1TPA City1TYS City2ATL City2AUS
## Min. :-0.04474 Min. :-0.03162 Min. :-0.05483 Min. :-0.03162
## 1st Qu.:-0.04474 1st Qu.:-0.03162 1st Qu.:-0.05483 1st Qu.:-0.03162
## Median :-0.04474 Median :-0.03162 Median :-0.05483 Median :-0.03162
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.04474 3rd Qu.:-0.03162 3rd Qu.:-0.05483 3rd Qu.:-0.03162
## Max. :22.32714 Max. :31.59115 Max. :18.22089 Max. :31.59115
## City2BDL City2BHM City2BNA City2BOS
## Min. :-0.1005 Min. :-0.03162 Min. :-0.1275 Min. :-0.04474
## 1st Qu.:-0.1005 1st Qu.:-0.03162 1st Qu.:-0.1275 1st Qu.:-0.04474
## Median :-0.1005 Median :-0.03162 Median :-0.1275 Median :-0.04474
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.:-0.1005 3rd Qu.:-0.03162 3rd Qu.:-0.1275 3rd Qu.:-0.04474
## Max. : 9.9449 Max. :31.59115 Max. : 7.8383 Max. :22.32714
## City2BUF City2BWI City2CLE City2CLT
## Min. :-0.04474 Min. :-0.06334 Min. :-0.06334 Min. :-0.05483
## 1st Qu.:-0.04474 1st Qu.:-0.06334 1st Qu.:-0.06334 1st Qu.:-0.05483
## Median :-0.04474 Median :-0.06334 Median :-0.06334 Median :-0.05483
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.04474 3rd Qu.:-0.06334 3rd Qu.:-0.06334 3rd Qu.:-0.05483
## Max. :22.32714 Max. :15.77184 Max. :15.77184 Max. :18.22089
## City2CMH City2CVG City2DAY City2DEN
## Min. :-0.06334 Min. :-0.05483 Min. :-0.06334 Min. :-0.1102
## 1st Qu.:-0.06334 1st Qu.:-0.05483 1st Qu.:-0.06334 1st Qu.:-0.1102
## Median :-0.06334 Median :-0.05483 Median :-0.06334 Median :-0.1102
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.06334 3rd Qu.:-0.05483 3rd Qu.:-0.06334 3rd Qu.:-0.1102
## Max. :15.77184 Max. :18.22089 Max. :15.77184 Max. : 9.0692
## City2DFW City2DSM City2DTW City2ELP
## Min. :-0.1275 Min. :-0.03162 Min. :-0.08392 Min. :-0.05483
## 1st Qu.:-0.1275 1st Qu.:-0.03162 1st Qu.:-0.08392 1st Qu.:-0.05483
## Median :-0.1275 Median :-0.03162 Median :-0.08392 Median :-0.05483
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.1275 3rd Qu.:-0.03162 3rd Qu.:-0.08392 3rd Qu.:-0.05483
## Max. : 7.8383 Max. :31.59115 Max. :11.90442 Max. :18.22089
## City2FLL City2FNT City2GEG City2GPT
## Min. :-0.1191 Min. :-0.03162 Min. :-0.09525 Min. :-0.03162
## 1st Qu.:-0.1191 1st Qu.:-0.03162 1st Qu.:-0.09525 1st Qu.:-0.03162
## Median :-0.1191 Median :-0.03162 Median :-0.09525 Median :-0.03162
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.1191 3rd Qu.:-0.03162 3rd Qu.:-0.09525 3rd Qu.:-0.03162
## Max. : 8.3880 Max. :31.59115 Max. :10.48814 Max. :31.59115
## City2GSO City2HPN City2HRL City2IAD
## Min. :-0.05483 Min. :-0.03162 Min. :-0.03162 Min. :-0.204
## 1st Qu.:-0.05483 1st Qu.:-0.03162 1st Qu.:-0.03162 1st Qu.:-0.204
## Median :-0.05483 Median :-0.03162 Median :-0.03162 Median :-0.204
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.000
## 3rd Qu.:-0.05483 3rd Qu.:-0.03162 3rd Qu.:-0.03162 3rd Qu.:-0.204
## Max. :18.22089 Max. :31.59115 Max. :31.59115 Max. : 4.897
## City2IAH City2ICT City2IND City2ISP
## Min. :-0.1428 Min. :-0.03162 Min. :-0.09525 Min. :-0.05483
## 1st Qu.:-0.1428 1st Qu.:-0.03162 1st Qu.:-0.09525 1st Qu.:-0.05483
## Median :-0.1428 Median :-0.03162 Median :-0.09525 Median :-0.05483
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.1428 3rd Qu.:-0.03162 3rd Qu.:-0.09525 3rd Qu.:-0.05483
## Max. : 6.9965 Max. :31.59115 Max. :10.48814 Max. :18.22089
## City2JAN City2JAX City2LAS City2LAX
## Min. :-0.04474 Min. :-0.08976 Min. :-0.1633 Min. :-0.1665
## 1st Qu.:-0.04474 1st Qu.:-0.08976 1st Qu.:-0.1633 1st Qu.:-0.1665
## Median :-0.04474 Median :-0.08976 Median :-0.1633 Median :-0.1665
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.04474 3rd Qu.:-0.08976 3rd Qu.:-0.1633 3rd Qu.:-0.1665
## Max. :22.32714 Max. :11.12996 Max. : 6.1175 Max. : 6.0001
## City2LBB City2LGA City2LGB City2LIT
## Min. :-0.05483 Min. :-0.2093 Min. :-0.04474 Min. :-0.05483
## 1st Qu.:-0.05483 1st Qu.:-0.2093 1st Qu.:-0.04474 1st Qu.:-0.05483
## Median :-0.05483 Median :-0.2093 Median :-0.04474 Median :-0.05483
## Mean : 0.00000 Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.05483 3rd Qu.:-0.2093 3rd Qu.:-0.04474 3rd Qu.:-0.05483
## Max. :18.22089 Max. : 4.7735 Max. :22.32714 Max. :18.22089
## City2MAF City2MCI City2MCO City2MDT
## Min. :-0.04474 Min. :-0.1054 Min. :-0.2119 Min. :-0.03162
## 1st Qu.:-0.04474 1st Qu.:-0.1054 1st Qu.:-0.2119 1st Qu.:-0.03162
## Median :-0.04474 Median :-0.1054 Median :-0.2119 Median :-0.03162
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.:-0.04474 3rd Qu.:-0.1054 3rd Qu.:-0.2119 3rd Qu.:-0.03162
## Max. :22.32714 Max. : 9.4773 Max. : 4.7152 Max. :31.59115
## City2MEM City2MHT City2MIA City2MKE
## Min. :-0.08392 Min. :-0.07085 Min. :-0.1102 Min. :-0.1005
## 1st Qu.:-0.08392 1st Qu.:-0.07085 1st Qu.:-0.1102 1st Qu.:-0.1005
## Median :-0.08392 Median :-0.07085 Median :-0.1102 Median :-0.1005
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.08392 3rd Qu.:-0.07085 3rd Qu.:-0.1102 3rd Qu.:-0.1005
## Max. :11.90442 Max. :14.09968 Max. : 9.0692 Max. : 9.9449
## City2MSP City2MSY City2OAK City2OKC
## Min. :-0.160 Min. :-0.1391 Min. :-0.1275 Min. :-0.1005
## 1st Qu.:-0.160 1st Qu.:-0.1391 1st Qu.:-0.1275 1st Qu.:-0.1005
## Median :-0.160 Median :-0.1391 Median :-0.1275 Median :-0.1005
## Mean : 0.000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.160 3rd Qu.:-0.1391 3rd Qu.:-0.1275 3rd Qu.:-0.1005
## Max. : 6.242 Max. : 7.1819 Max. : 7.8383 Max. : 9.9449
## City2OMA City2ONT City2ORD City2ORF
## Min. :-0.08976 Min. :-0.1005 Min. :-0.09525 Min. :-0.08976
## 1st Qu.:-0.08976 1st Qu.:-0.1005 1st Qu.:-0.09525 1st Qu.:-0.08976
## Median :-0.08976 Median :-0.1005 Median :-0.09525 Median :-0.08976
## Mean : 0.00000 Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.08976 3rd Qu.:-0.1005 3rd Qu.:-0.09525 3rd Qu.:-0.08976
## Max. :11.12996 Max. : 9.9449 Max. :10.48814 Max. :11.12996
## City2PBI City2PDX City2PHF City2PHL
## Min. :-0.1191 Min. :-0.1499 Min. :-0.03162 Min. :-0.1534
## 1st Qu.:-0.1191 1st Qu.:-0.1499 1st Qu.:-0.03162 1st Qu.:-0.1534
## Median :-0.1191 Median :-0.1499 Median :-0.03162 Median :-0.1534
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.1191 3rd Qu.:-0.1499 3rd Qu.:-0.03162 3rd Qu.:-0.1534
## Max. : 8.3880 Max. : 6.6641 Max. :31.59115 Max. : 6.5143
## City2PHX City2PIT City2PNS City2PVD
## Min. :-0.1931 Min. :-0.1314 Min. :-0.03162 Min. :-0.1102
## 1st Qu.:-0.1931 1st Qu.:-0.1314 1st Qu.:-0.03162 1st Qu.:-0.1102
## Median :-0.1931 Median :-0.1314 Median :-0.03162 Median :-0.1102
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.1931 3rd Qu.:-0.1314 3rd Qu.:-0.03162 3rd Qu.:-0.1102
## Max. : 5.1721 Max. : 7.6004 Max. :31.59115 Max. : 9.0692
## City2PWM City2RDU City2RIC City2RNO
## Min. :-0.03162 Min. :-0.1534 Min. :-0.06334 Min. :-0.09525
## 1st Qu.:-0.03162 1st Qu.:-0.1534 1st Qu.:-0.06334 1st Qu.:-0.09525
## Median :-0.03162 Median :-0.1534 Median :-0.06334 Median :-0.09525
## Mean : 0.00000 Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.03162 3rd Qu.:-0.1534 3rd Qu.:-0.06334 3rd Qu.:-0.09525
## Max. :31.59115 Max. : 6.5143 Max. :15.77184 Max. :10.48814
## City2ROC City2RSW City2SAN City2SAT
## Min. :-0.07085 Min. :-0.09525 Min. :-0.1931 Min. :-0.1353
## 1st Qu.:-0.07085 1st Qu.:-0.09525 1st Qu.:-0.1931 1st Qu.:-0.1353
## Median :-0.07085 Median :-0.09525 Median :-0.1931 Median :-0.1353
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.07085 3rd Qu.:-0.09525 3rd Qu.:-0.1931 3rd Qu.:-0.1353
## Max. :14.09968 Max. :10.48814 Max. : 5.1721 Max. : 7.3825
## City2SAV City2SDF City2SEA City2SFO
## Min. :-0.04474 Min. :-0.07765 Min. :-0.2093 Min. :-0.1788
## 1st Qu.:-0.04474 1st Qu.:-0.07765 1st Qu.:-0.2093 1st Qu.:-0.1788
## Median :-0.04474 Median :-0.07765 Median :-0.2093 Median :-0.1788
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.04474 3rd Qu.:-0.07765 3rd Qu.:-0.2093 3rd Qu.:-0.1788
## Max. :22.32714 Max. :12.86472 Max. : 4.7735 Max. : 5.5881
## City2SJC City2SLC City2SMF City2SNA
## Min. :-0.1499 Min. :-0.1428 Min. :-0.1314 Min. :-0.1391
## 1st Qu.:-0.1499 1st Qu.:-0.1428 1st Qu.:-0.1314 1st Qu.:-0.1391
## Median :-0.1499 Median :-0.1428 Median :-0.1314 Median :-0.1391
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.1499 3rd Qu.:-0.1428 3rd Qu.:-0.1314 3rd Qu.:-0.1391
## Max. : 6.6641 Max. : 6.9965 Max. : 7.6004 Max. : 7.1819
## City2SRQ City2STL City2SYR City2TLH
## Min. :-0.04474 Min. :-0.1788 Min. :-0.05483 Min. :-0.04474
## 1st Qu.:-0.04474 1st Qu.:-0.1788 1st Qu.:-0.05483 1st Qu.:-0.04474
## Median :-0.04474 Median :-0.1788 Median :-0.05483 Median :-0.04474
## Mean : 0.00000 Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.:-0.04474 3rd Qu.:-0.1788 3rd Qu.:-0.05483 3rd Qu.:-0.04474
## Max. :22.32714 Max. : 5.5881 Max. :18.22089 Max. :22.32714
## City2TPA City2TUL City2TUS
## Min. :-0.2195 Min. :-0.07765 Min. :-0.1102
## 1st Qu.:-0.2195 1st Qu.:-0.07765 1st Qu.:-0.1102
## Median :-0.2195 Median :-0.07765 Median :-0.1102
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.2195 3rd Qu.:-0.07765 3rd Qu.:-0.1102
## Max. : 4.5517 Max. :12.86472 Max. : 9.0692
## Market.Leading.Airline..MLA.AA Market.Leading.Airline..MLA.AS
## Min. :-0.3949 Min. :-0.1817
## 1st Qu.:-0.3949 1st Qu.:-0.1817
## Median :-0.3949 Median :-0.1817
## Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.3949 3rd Qu.:-0.1817
## Max. : 2.5300 Max. : 5.4972
## Market.Leading.Airline..MLA.B6 Market.Leading.Airline..MLA.CO
## Min. :-0.1191 Min. :-0.2721
## 1st Qu.:-0.1191 1st Qu.:-0.2721
## Median :-0.1191 Median :-0.2721
## Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.1191 3rd Qu.:-0.2721
## Max. : 8.3880 Max. : 3.6714
## Market.Leading.Airline..MLA.DL Market.Leading.Airline..MLA.F9
## Min. :-0.4395 Min. :-0.05483
## 1st Qu.:-0.4395 1st Qu.:-0.05483
## Median :-0.4395 Median :-0.05483
## Mean : 0.0000 Mean : 0.00000
## 3rd Qu.:-0.4395 3rd Qu.:-0.05483
## Max. : 2.2733 Max. :18.22089
## Market.Leading.Airline..MLA.FL Market.Leading.Airline..MLA.G4
## Min. :-0.1275 Min. :-0.03162
## 1st Qu.:-0.1275 1st Qu.:-0.03162
## Median :-0.1275 Median :-0.03162
## Mean : 0.0000 Mean : 0.00000
## 3rd Qu.:-0.1275 3rd Qu.:-0.03162
## Max. : 7.8383 Max. :31.59115
## Market.Leading.Airline..MLA.HP Market.Leading.Airline..MLA.NK
## Min. :-0.1758 Min. :-0.1005
## 1st Qu.:-0.1758 1st Qu.:-0.1005
## Median :-0.1758 Median :-0.1005
## Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.1758 3rd Qu.:-0.1005
## Max. : 5.6834 Max. : 9.9449
## Market.Leading.Airline..MLA.NW Market.Leading.Airline..MLA.TZ
## Min. :-0.3066 Min. :-0.1005
## 1st Qu.:-0.3066 1st Qu.:-0.1005
## Median :-0.3066 Median :-0.1005
## Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.3066 3rd Qu.:-0.1005
## Max. : 3.2584 Max. : 9.9449
## Market.Leading.Airline..MLA.UA Market.Leading.Airline..MLA.US
## Min. :-0.3162 Min. :-0.27
## 1st Qu.:-0.3162 1st Qu.:-0.27
## Median :-0.3162 Median :-0.27
## Mean : 0.0000 Mean : 0.00
## 3rd Qu.:-0.3162 3rd Qu.:-0.27
## Max. : 3.1590 Max. : 3.70
## Market.Leading.Airline..MLA.WN Market.Leading.Airline..MLA.YX
## Min. :-0.5878 Min. :-0.1275
## 1st Qu.:-0.5878 1st Qu.:-0.1275
## Median :-0.5878 Median :-0.1275
## Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 1.6995 3rd Qu.:-0.1275
## Max. : 1.6995 Max. : 7.8383
## Low.Price.Airline..LPA.AA Low.Price.Airline..LPA.AQ Low.Price.Airline..LPA.AS
## Min. :-0.3847 Min. :-0.04474 Min. :-0.1665
## 1st Qu.:-0.3847 1st Qu.:-0.04474 1st Qu.:-0.1665
## Median :-0.3847 Median :-0.04474 Median :-0.1665
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.3847 3rd Qu.:-0.04474 3rd Qu.:-0.1665
## Max. : 2.5971 Max. :22.32714 Max. : 6.0001
## Low.Price.Airline..LPA.AX Low.Price.Airline..LPA.B6 Low.Price.Airline..LPA.CO
## Min. :-0.05483 Min. :-0.1233 Min. :-0.217
## 1st Qu.:-0.05483 1st Qu.:-0.1233 1st Qu.:-0.217
## Median :-0.05483 Median :-0.1233 Median :-0.217
## Mean : 0.00000 Mean : 0.0000 Mean : 0.000
## 3rd Qu.:-0.05483 3rd Qu.:-0.1233 3rd Qu.:-0.217
## Max. :18.22089 Max. : 8.0994 Max. : 4.604
## Low.Price.Airline..LPA.DL Low.Price.Airline..LPA.F9 Low.Price.Airline..LPA.FL
## Min. :-0.4215 Min. :-0.1758 Min. :-0.2525
## 1st Qu.:-0.4215 1st Qu.:-0.1758 1st Qu.:-0.2525
## Median :-0.4215 Median :-0.1758 Median :-0.2525
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.:-0.4215 3rd Qu.:-0.1758 3rd Qu.:-0.2525
## Max. : 2.3700 Max. : 5.6834 Max. : 3.9561
## Low.Price.Airline..LPA.G4 Low.Price.Airline..LPA.HP Low.Price.Airline..LPA.NK
## Min. :-0.03162 Min. :-0.248 Min. :-0.1275
## 1st Qu.:-0.03162 1st Qu.:-0.248 1st Qu.:-0.1275
## Median :-0.03162 Median :-0.248 Median :-0.1275
## Mean : 0.00000 Mean : 0.000 Mean : 0.0000
## 3rd Qu.:-0.03162 3rd Qu.:-0.248 3rd Qu.:-0.1275
## Max. :31.59115 Max. : 4.028 Max. : 7.8383
## Low.Price.Airline..LPA.NW Low.Price.Airline..LPA.SY Low.Price.Airline..LPA.TZ
## Min. :-0.1846 Min. :-0.08392 Min. :-0.1875
## 1st Qu.:-0.1846 1st Qu.:-0.08392 1st Qu.:-0.1875
## Median :-0.1846 Median :-0.08392 Median :-0.1875
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:-0.1846 3rd Qu.:-0.08392 3rd Qu.:-0.1875
## Max. : 5.4105 Max. :11.90442 Max. : 5.3276
## Low.Price.Airline..LPA.UA Low.Price.Airline..LPA.US Low.Price.Airline..LPA.WN
## Min. :-0.2411 Min. :-0.335 Min. :-0.5463
## 1st Qu.:-0.2411 1st Qu.:-0.335 1st Qu.:-0.5463
## Median :-0.2411 Median :-0.335 Median :-0.5463
## Mean : 0.0000 Mean : 0.000 Mean : 0.0000
## 3rd Qu.:-0.2411 3rd Qu.:-0.335 3rd Qu.:-0.5463
## Max. : 4.1430 Max. : 2.982 Max. : 1.8288
## Low.Price.Airline..LPA.YX
## Min. :-0.05483
## 1st Qu.:-0.05483
## Median :-0.05483
## Mean : 0.00000
## 3rd Qu.:-0.05483
## Max. :18.22089#The variables were standardized to achieve mean centering and unit variance.
#This was verified by examining the summary statistics, which showed that the mean was near 0 and the standard deviation was near 1.
#This indicates that the variables are appropriately scaled for modeling.#Multiple Linear Regression
#Data Partitioning for Model 1
set.seed(2345)
n1_mixed<-air1[order(runif(1000)),]
n1_train<-air1[1:700,] #70 percent of data used for training the model
n1_test<-air1[701:1000,] #remaining 30 percent of data is used for testing the model
#Model 1
n_full1 <- lm(Average.Flight.Fare~.,data = n1_train)
summary(n_full1)##
## Call:
## lm(formula = Average.Flight.Fare ~ ., data = n1_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.44400 -0.07015 0.00000 0.05745 0.58104
##
## Coefficients: (47 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.591e-02 4.491e-02 1.022 0.307115
## Distance 7.228e-02 1.237e-02 5.845 8.87e-09 ***
## Average.Weekly.Passengers -2.352e-02 9.778e-03 -2.405 0.016515 *
## Market.Share.MLA 3.958e-02 8.968e-03 4.414 1.23e-05 ***
## Average.MLA.Fare 7.958e-01 1.695e-02 46.957 < 2e-16 ***
## Market.Share.LPA -2.456e-02 9.148e-03 -2.685 0.007491 **
## Average.LPA.Fare 1.446e-01 1.283e-02 11.271 < 2e-16 ***
## City1ABQ -2.132e-02 2.017e-02 -1.057 0.291041
## City1ACY -2.346e-02 1.147e-02 -2.045 0.041332 *
## City1ALB -1.888e-02 1.395e-02 -1.354 0.176371
## City1AMA -1.413e-02 8.009e-03 -1.764 0.078337 .
## City1ATL -4.966e-02 3.689e-02 -1.346 0.178855
## City1AUS -1.783e-02 2.173e-02 -0.821 0.412250
## City1BDL -2.097e-02 1.802e-02 -1.163 0.245235
## City1BHM -1.160e-02 1.381e-02 -0.840 0.401530
## City1BNA NA NA NA NA
## City1BOI -1.358e-02 1.409e-02 -0.964 0.335541
## City1BOS -3.096e-02 2.914e-02 -1.063 0.288414
## City1BTV -6.818e-03 6.867e-03 -0.993 0.321271
## City1BUF -1.947e-02 1.459e-02 -1.335 0.182529
## City1BUR -1.896e-02 1.574e-02 -1.204 0.228972
## City1BWI -3.264e-02 3.222e-02 -1.013 0.311473
## City1CAE -6.816e-03 6.194e-03 -1.100 0.271645
## City1CAK -7.962e-03 9.501e-03 -0.838 0.402410
## City1CHS -8.432e-03 6.195e-03 -1.361 0.174038
## City1CLE 3.173e-04 2.281e-02 0.014 0.988905
## City1CLT -2.644e-02 2.053e-02 -1.288 0.198377
## City1CMH -2.866e-02 2.015e-02 -1.423 0.155383
## City1COS -6.340e-03 9.794e-03 -0.647 0.517694
## City1CRP -1.206e-02 7.983e-03 -1.511 0.131353
## City1CVG -1.142e-02 1.651e-02 -0.692 0.489550
## City1DAY -1.315e-02 9.069e-03 -1.450 0.147512
## City1DEN -1.252e-02 2.889e-02 -0.434 0.664811
## City1DFW -2.618e-02 3.404e-02 -0.769 0.442065
## City1DSM -7.918e-03 7.919e-03 -1.000 0.317839
## City1DTW -1.386e-02 2.804e-02 -0.494 0.621267
## City1ELP -1.700e-02 1.228e-02 -1.384 0.166890
## City1EUG -6.375e-03 6.942e-03 -0.918 0.358838
## City1FLL -3.778e-02 2.555e-02 -1.478 0.139882
## City1FNT -8.334e-03 7.265e-03 -1.147 0.251854
## City1GPT -5.251e-03 7.310e-03 -0.718 0.472840
## City1GRR -2.030e-02 1.043e-02 -1.947 0.052051 .
## City1GSO -1.437e-02 9.093e-03 -1.581 0.114589
## City1GSP 3.307e-04 6.323e-03 0.052 0.958317
## City1HRL -1.246e-02 6.529e-03 -1.909 0.056833 .
## City1IAD NA NA NA NA
## City1IAH 8.298e-03 2.978e-02 0.279 0.780625
## City1IND -2.370e-02 1.678e-02 -1.412 0.158481
## City1ISP NA NA NA NA
## City1JAX -1.408e-02 1.306e-02 -1.078 0.281446
## City1LAS -3.528e-02 2.892e-02 -1.220 0.223045
## City1LAX NA NA NA NA
## City1LGA NA NA NA NA
## City1LGB -3.051e-02 1.280e-02 -2.383 0.017535 *
## City1LIT -6.775e-03 6.523e-03 -1.039 0.299421
## City1MCI -1.290e-02 2.267e-02 -0.569 0.569663
## City1MCO NA NA NA NA
## City1MDT -1.002e-02 6.577e-03 -1.524 0.128198
## City1MEM NA NA NA NA
## City1MHT NA NA NA NA
## City1MIA NA NA NA NA
## City1MKE NA NA NA NA
## City1MSN NA NA NA NA
## City1MSP NA NA NA NA
## City1MSY NA NA NA NA
## City1MYR NA NA NA NA
## City1OAK NA NA NA NA
## City1OKC NA NA NA NA
## City1OMA NA NA NA NA
## City1ONT NA NA NA NA
## City1ORD -1.231e-03 3.725e-02 -0.033 0.973641
## City1ORF NA NA NA NA
## City1PDX NA NA NA NA
## City1PHF NA NA NA NA
## City1PHL NA NA NA NA
## City1PHX NA NA NA NA
## City1PIT NA NA NA NA
## City1PSP -1.227e-02 9.889e-03 -1.241 0.215248
## City1PVD NA NA NA NA
## City1RDU NA NA NA NA
## City1RNO NA NA NA NA
## City1ROC NA NA NA NA
## City1RSW -2.656e-02 1.411e-02 -1.882 0.060439 .
## City1SAN NA NA NA NA
## City1SAT NA NA NA NA
## City1SDF NA NA NA NA
## City1SEA NA NA NA NA
## City1SFO NA NA NA NA
## City1SJC NA NA NA NA
## City1SLC NA NA NA NA
## City1SMF NA NA NA NA
## City1SNA NA NA NA NA
## City1STL NA NA NA NA
## City1SYR NA NA NA NA
## City1TLH NA NA NA NA
## City1TPA NA NA NA NA
## City1TYS NA NA NA NA
## City2ATL -3.140e-04 6.194e-03 -0.051 0.959593
## City2AUS 1.170e-04 4.845e-03 0.024 0.980749
## City2BDL 3.176e-03 7.389e-03 0.430 0.667487
## City2BHM 2.531e-04 4.809e-03 0.053 0.958054
## City2BNA -1.014e-02 9.380e-03 -1.081 0.280050
## City2BOS 5.655e-03 5.691e-03 0.994 0.320811
## City2BUF -3.094e-03 5.155e-03 -0.600 0.548653
## City2BWI -6.709e-04 5.895e-03 -0.114 0.909431
## City2CLE 1.531e-02 5.874e-03 2.606 0.009421 **
## City2CLT 6.637e-03 5.842e-03 1.136 0.256389
## City2CMH 7.779e-03 5.953e-03 1.307 0.191880
## City2CVG 1.890e-02 5.726e-03 3.300 0.001032 **
## City2DAY -4.894e-03 5.867e-03 -0.834 0.404490
## City2DEN 8.777e-03 8.259e-03 1.063 0.288397
## City2DFW 9.122e-03 8.992e-03 1.014 0.310842
## City2DSM 6.468e-03 4.905e-03 1.319 0.187815
## City2DTW 4.032e-03 7.239e-03 0.557 0.577725
## City2ELP -6.744e-03 5.503e-03 -1.226 0.220908
## City2FLL -1.165e-03 8.483e-03 -0.137 0.890866
## City2FNT 1.936e-03 5.866e-03 0.330 0.741543
## City2GEG -4.521e-03 1.119e-02 -0.404 0.686456
## City2GPT 4.649e-03 5.865e-03 0.793 0.428335
## City2GSO 4.693e-03 5.535e-03 0.848 0.396932
## City2HPN 1.088e-03 4.886e-03 0.223 0.823897
## City2HRL -1.724e-03 4.859e-03 -0.355 0.722834
## City2IAD 1.626e-02 1.388e-02 1.171 0.242058
## City2IAH 2.131e-02 9.919e-03 2.149 0.032121 *
## City2ICT 5.466e-03 5.863e-03 0.932 0.351605
## City2IND -1.242e-04 7.201e-03 -0.017 0.986242
## City2ISP -5.729e-03 5.511e-03 -1.039 0.299079
## City2JAN -4.939e-03 5.217e-03 -0.947 0.344269
## City2JAX -4.118e-03 6.897e-03 -0.597 0.550757
## City2LAS -8.819e-03 1.090e-02 -0.809 0.418726
## City2LAX 1.261e-03 1.110e-02 0.114 0.909636
## City2LBB -9.044e-03 5.568e-03 -1.624 0.104936
## City2LGA 2.808e-02 1.543e-02 1.820 0.069377 .
## City2LGB -4.468e-03 5.542e-03 -0.806 0.420562
## City2LIT -8.113e-03 5.576e-03 -1.455 0.146247
## City2MAF -6.399e-03 5.221e-03 -1.226 0.220890
## City2MCI -3.406e-03 7.595e-03 -0.448 0.654049
## City2MCO 8.624e-05 1.353e-02 0.006 0.994917
## City2MDT 6.636e-03 4.906e-03 1.353 0.176730
## City2MEM 1.319e-03 7.072e-03 0.187 0.852076
## City2MHT -9.846e-04 6.441e-03 -0.153 0.878570
## City2MIA 2.104e-04 7.905e-03 0.027 0.978776
## City2MKE 1.213e-02 8.781e-03 1.381 0.167762
## City2MSP 2.305e-02 1.246e-02 1.849 0.064943 .
## City2MSY -5.863e-03 9.442e-03 -0.621 0.534884
## City2OAK 7.096e-03 9.236e-03 0.768 0.442676
## City2OKC -5.120e-03 7.576e-03 -0.676 0.499485
## City2OMA 3.348e-03 7.251e-03 0.462 0.644457
## City2ONT 6.201e-03 7.835e-03 0.791 0.429042
## City2ORD 1.334e-02 7.429e-03 1.795 0.073179 .
## City2ORF 4.719e-03 7.042e-03 0.670 0.503035
## City2PBI -8.773e-03 9.413e-03 -0.932 0.351799
## City2PDX 4.686e-03 1.042e-02 0.450 0.652970
## City2PHF 1.913e-03 5.858e-03 0.327 0.744161
## City2PHL 5.303e-04 1.119e-02 0.047 0.962230
## City2PHX -1.258e-02 1.284e-02 -0.979 0.327851
## City2PIT 8.810e-03 1.073e-02 0.821 0.411951
## City2PNS -2.020e-03 4.845e-03 -0.417 0.676957
## City2PVD 3.440e-04 8.666e-03 0.040 0.968348
## City2PWM NA NA NA NA
## City2RDU -2.625e-03 1.052e-02 -0.250 0.803052
## City2RIC 1.332e-02 6.374e-03 2.089 0.037142 *
## City2RNO -6.397e-03 8.642e-03 -0.740 0.459513
## City2ROC 2.048e-03 7.504e-03 0.273 0.785065
## City2RSW -5.158e-03 7.461e-03 -0.691 0.489731
## City2SAN 4.064e-03 1.287e-02 0.316 0.752324
## City2SAT 1.830e-04 9.832e-03 0.019 0.985161
## City2SAV -5.697e-03 6.869e-03 -0.829 0.407269
## City2SDF 4.324e-03 6.307e-03 0.686 0.493221
## City2SEA 1.425e-03 1.416e-02 0.101 0.919868
## City2SFO 3.000e-03 1.246e-02 0.241 0.809888
## City2SJC 7.418e-03 1.078e-02 0.688 0.491812
## City2SLC -5.016e-04 1.055e-02 -0.048 0.962099
## City2SMF 5.028e-03 9.766e-03 0.515 0.606878
## City2SNA 1.361e-02 1.071e-02 1.271 0.204242
## City2SRQ -9.520e-03 7.216e-03 -1.319 0.187624
## City2STL 5.679e-04 1.189e-02 0.048 0.961932
## City2SYR 1.553e-02 8.480e-03 1.831 0.067590 .
## City2TLH -3.472e-03 6.848e-03 -0.507 0.612394
## City2TPA -1.732e-03 1.416e-02 -0.122 0.902662
## City2TUL -6.204e-03 7.052e-03 -0.880 0.379412
## City2TUS NA NA NA NA
## Market.Leading.Airline..MLA.AA 5.090e-02 2.420e-02 2.104 0.035882 *
## Market.Leading.Airline..MLA.AS 1.184e-02 1.849e-02 0.641 0.522122
## Market.Leading.Airline..MLA.B6 3.100e-02 1.772e-02 1.750 0.080727 .
## Market.Leading.Airline..MLA.CO -1.402e-02 1.913e-02 -0.733 0.463867
## Market.Leading.Airline..MLA.DL 6.440e-02 2.602e-02 2.475 0.013642 *
## Market.Leading.Airline..MLA.F9 2.300e-02 6.094e-03 3.774 0.000179 ***
## Market.Leading.Airline..MLA.FL 1.393e-02 1.530e-02 0.911 0.362902
## Market.Leading.Airline..MLA.G4 1.724e-02 7.450e-03 2.314 0.021053 *
## Market.Leading.Airline..MLA.HP 1.381e-02 1.329e-02 1.039 0.299143
## Market.Leading.Airline..MLA.NK 3.180e-02 1.141e-02 2.786 0.005520 **
## Market.Leading.Airline..MLA.NW 3.046e-02 2.275e-02 1.339 0.181194
## Market.Leading.Airline..MLA.TZ 2.757e-02 8.847e-03 3.116 0.001933 **
## Market.Leading.Airline..MLA.UA -1.269e-02 2.110e-02 -0.601 0.547850
## Market.Leading.Airline..MLA.US 4.758e-02 1.983e-02 2.399 0.016781 *
## Market.Leading.Airline..MLA.WN 4.486e-02 3.043e-02 1.474 0.140953
## Market.Leading.Airline..MLA.YX NA NA NA NA
## Low.Price.Airline..LPA.AA -6.943e-03 5.215e-02 -0.133 0.894139
## Low.Price.Airline..LPA.AQ -3.845e-03 8.453e-03 -0.455 0.649372
## Low.Price.Airline..LPA.AS 1.296e-02 2.803e-02 0.462 0.644114
## Low.Price.Airline..LPA.AX 1.127e-02 9.945e-03 1.134 0.257484
## Low.Price.Airline..LPA.B6 -1.291e-02 2.521e-02 -0.512 0.608749
## Low.Price.Airline..LPA.CO 5.629e-03 3.268e-02 0.172 0.863300
## Low.Price.Airline..LPA.DL -4.262e-03 5.560e-02 -0.077 0.938924
## Low.Price.Airline..LPA.F9 -6.236e-03 2.645e-02 -0.236 0.813675
## Low.Price.Airline..LPA.FL -1.685e-02 3.731e-02 -0.452 0.651773
## Low.Price.Airline..LPA.G4 NA NA NA NA
## Low.Price.Airline..LPA.HP -8.212e-03 3.666e-02 -0.224 0.822820
## Low.Price.Airline..LPA.NK -1.424e-02 2.062e-02 -0.690 0.490213
## Low.Price.Airline..LPA.NW -2.981e-04 2.804e-02 -0.011 0.991523
## Low.Price.Airline..LPA.SY -1.700e-02 1.588e-02 -1.070 0.284901
## Low.Price.Airline..LPA.TZ -2.109e-03 2.895e-02 -0.073 0.941953
## Low.Price.Airline..LPA.UA -7.534e-03 3.589e-02 -0.210 0.833803
## Low.Price.Airline..LPA.US 1.024e-02 4.709e-02 0.217 0.827930
## Low.Price.Airline..LPA.WN -1.279e-02 6.565e-02 -0.195 0.845554
## Low.Price.Airline..LPA.YX NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1384 on 530 degrees of freedom
## Multiple R-squared: 0.9838, Adjusted R-squared: 0.9786
## F-statistic: 189.9 on 169 and 530 DF, p-value: < 2.2e-16# Predict and evaluate on test data
model1_pred <- predict(n_full1, newdata = n1_test)## Warning in predict.lm(n_full1, newdata = n1_test): prediction from
## rank-deficient fit; attr(*, "non-estim") has doubtful casesmodel1_pred## 701 702 703 704 705 706
## -0.700786781 -0.066990980 -0.519215287 -0.300738817 -1.686241816 -0.407644286
## 707 708 709 710 711 712
## -0.381787369 0.277006188 1.870783673 2.889046391 0.684393858 1.149603212
## 713 714 715 716 717 718
## 0.295150663 0.319062199 3.455745094 0.877543466 -1.386472935 0.346746627
## 719 720 721 722 723 724
## -0.079696373 1.311302716 1.504530314 -1.561690488 1.541446774 -0.522714693
## 725 726 727 728 729 730
## 0.214919068 0.547906559 -1.096996526 -1.272727963 1.043846743 -1.201759790
## 731 732 733 734 735 736
## 0.054683641 -0.880005120 -1.334730884 -0.256424646 -0.406441206 0.916157891
## 737 738 739 740 741 742
## -1.330424885 4.026276522 1.070776902 -0.496317531 -1.478950326 -0.553017011
## 743 744 745 746 747 748
## -0.358615541 -0.392980750 0.031017199 -0.272190899 1.448393453 0.801919486
## 749 750 751 752 753 754
## 0.091285174 0.526007066 0.002703601 0.795025265 1.124080355 0.184380836
## 755 756 757 758 759 760
## 0.126141233 0.327944229 0.230407744 0.260763849 -0.223286926 0.818455512
## 761 762 763 764 765 766
## 3.389240692 1.214230075 -0.927055640 -1.536322507 1.007274840 0.520593932
## 767 768 769 770 771 772
## 0.682006124 -0.560842851 0.007335849 0.475472807 -0.780447843 0.025948299
## 773 774 775 776 777 778
## 0.846556044 1.008718128 2.507448525 0.789983995 0.463310277 1.326855372
## 779 780 781 782 783 784
## 0.036312399 1.098592373 0.641378278 1.029667488 1.087160175 0.861039283
## 785 786 787 788 789 790
## 0.650284022 0.722914437 1.398262049 1.201275400 1.707652598 0.867918278
## 791 792 793 794 795 796
## 0.544125141 1.411492016 -0.647509022 -1.273086082 1.008639245 -0.603726781
## 797 798 799 800 801 802
## 1.181327217 0.273549475 -0.239370571 -1.073060133 -0.321314891 0.189509333
## 803 804 805 806 807 808
## 0.158780241 -0.525161201 0.393936144 0.366142343 -0.896789249 0.102991057
## 809 810 811 812 813 814
## 0.138178970 -0.063312198 -0.792076537 -0.100776283 0.184625791 -0.567447094
## 815 816 817 818 819 820
## 0.086524504 1.278408097 0.044754724 -0.931261656 0.663441040 -1.050037271
## 821 822 823 824 825 826
## 0.630285226 1.028697835 0.486703713 0.645959311 -0.768915155 1.115070959
## 827 828 829 830 831 832
## 0.343426738 -0.201422899 1.242895134 -0.136186971 1.030553492 -1.194425317
## 833 834 835 836 837 838
## 1.847335547 0.800221261 0.991446656 2.244848478 2.852790691 3.411877757
## 839 840 841 842 843 844
## 1.339745917 2.717634551 -0.117642409 -0.227438882 1.592322042 -0.928169449
## 845 846 847 848 849 850
## -0.596216029 1.581156258 -0.821859886 -0.410540045 -1.087315250 0.926507788
## 851 852 853 854 855 856
## -0.831949837 -1.348553171 0.729196389 -0.829348179 -0.919563532 -1.484591267
## 857 858 859 860 861 862
## -0.822731331 -1.197590537 -1.075859400 -0.915461959 -0.855143317 -0.269073488
## 863 864 865 866 867 868
## 0.275430286 0.049046797 -1.398171396 -0.492246804 -0.390184151 -1.597017226
## 869 870 871 872 873 874
## 0.457906558 0.670865118 -1.504481528 -0.375480672 -0.947735686 -1.266015659
## 875 876 877 878 879 880
## -0.767974560 -1.242016375 -0.182939393 -0.421363420 0.166717394 -0.243248739
## 881 882 883 884 885 886
## 0.402331238 -0.387090433 -0.658623333 -0.603909377 0.464150616 -0.061944530
## 887 888 889 890 891 892
## 0.979892952 -0.169606179 0.276316293 2.278303159 0.570322636 0.395412161
## 893 894 895 896 897 898
## -0.089142351 -0.326823479 0.802499266 0.462209625 0.170490972 1.437014837
## 899 900 901 902 903 904
## 1.227487832 2.259797790 1.883688893 2.013923959 2.010529430 -0.300301261
## 905 906 907 908 909 910
## -0.194544208 1.165862487 -0.459155537 0.280243067 0.150307579 -0.940415252
## 911 912 913 914 915 916
## -0.749202279 -0.139472013 -0.892578200 -0.265281447 -1.466078253 -0.575670451
## 917 918 919 920 921 922
## -0.771015277 -1.106862075 -0.455410843 -0.341278970 0.163216492 0.030915821
## 923 924 925 926 927 928
## 1.585453155 1.992514873 -0.265021859 -0.241942588 -1.368404981 -1.218778241
## 929 930 931 932 933 934
## 0.580217785 -0.667739208 -0.214879344 -0.803061650 -1.006378027 -0.272879952
## 935 936 937 938 939 940
## -0.366538507 -1.564450772 2.465939852 -0.603509020 0.531240198 -0.329446125
## 941 942 943 944 945 946
## 0.370913278 -0.274989315 0.131114573 0.083100097 1.100399122 0.094372004
## 947 948 949 950 951 952
## -1.009015017 -0.799590852 -1.382339947 -1.178915375 -0.399221916 0.867469605
## 953 954 955 956 957 958
## -0.525943095 -1.250952302 -1.069505796 -0.801421275 -0.662807358 0.180230527
## 959 960 961 962 963 964
## 0.217444744 0.573621353 1.001830244 1.425846643 1.090839575 -0.004952872
## 965 966 967 968 969 970
## -1.470134654 0.692577824 -0.504887051 -0.134786076 -0.058941700 -0.708570894
## 971 972 973 974 975 976
## -0.679254276 -0.937352573 2.044606500 0.202761372 0.316365808 0.746816392
## 977 978 979 980 981 982
## -0.680694001 -1.176607216 -0.035937555 0.381114114 -1.356414310 3.935276827
## 983 984 985 986 987 988
## -0.400727158 -0.685389312 1.756571403 4.468743749 -1.277833807 -0.835578019
## 989 990 991 992 993 994
## -0.166588577 3.352249920 -0.097710902 -1.507641138 0.057505626 -0.366592897
## 995 996 997 998 999 1000
## 2.662631966 -0.297488108 -1.253150621 0.207147114 -1.433092483 -0.426918068
## attr(,"non-estim")
## 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718
## 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750
## 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766
## 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
## 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782
## 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82
## 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798
## 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98
## 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814
## 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114
## 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830
## 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130
## 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846
## 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146
## 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862
## 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878
## 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178
## 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894
## 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194
## 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910
## 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210
## 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926
## 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226
## 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942
## 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242
## 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958
## 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258
## 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974
## 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274
## 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990
## 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290
## 991 992 993 994 995 996 997 998 999 1000
## 291 292 293 294 295 296 297 298 299 300# Calculate performance metrics
n_full1_r2 <- summary(n_full1)$r.squared
model1_test_r2 <- cor(n1_test$Average.Flight.Fare, model1_pred)^2#Model 2
#Data Partitioning for Model 2
set.seed(2345)
n_mixed<-air[order(runif(1000)),]
n_train<-air[1:700,] #70 percent of data used for training the model
n_test<-air[701:1000,] #remaining 30 percent of data is used for testing the model
#Model 2
n_full <- lm(Average.Flight.Fare~.,data = n_train)
summary(n_full)##
## Call:
## lm(formula = Average.Flight.Fare ~ ., data = n_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73500 -0.08710 -0.02160 0.07411 0.71608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.002721 0.006044 0.450 0.652702
## Distance 0.031665 0.008500 3.725 0.000211 ***
## Average.Weekly.Passengers -0.030405 0.006102 -4.983 7.91e-07 ***
## Market.Share.MLA 0.026191 0.007404 3.537 0.000431 ***
## Average.MLA.Fare 0.843170 0.013146 64.141 < 2e-16 ***
## Market.Share.LPA -0.017540 0.007773 -2.257 0.024340 *
## Average.LPA.Fare 0.154518 0.011965 12.914 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1594 on 693 degrees of freedom
## Multiple R-squared: 0.9732, Adjusted R-squared: 0.973
## F-statistic: 4195 on 6 and 693 DF, p-value: < 2.2e-16# Predict and evaluate on test data
model2_pred <- predict(n_full, newdata = n_test)
model2_pred## 759 760 761 762 763 764
## -0.393716732 0.672589234 3.353043107 0.908080775 -1.050687103 -1.680651088
## 765 766 767 768 769 770
## 0.783503738 0.269233639 0.534097604 -0.603832846 -0.091164574 0.431484738
## 771 772 773 774 776 777
## -0.920636060 -0.033335246 0.856898255 1.032443358 0.590621156 0.269613999
## 778 779 780 781 782 783
## 1.204256516 0.078399973 0.925450982 0.651794489 0.912803449 0.958318547
## 784 785 786 787 788 789
## 0.774163629 0.554432331 0.743412747 1.260108955 1.048374241 1.551634541
## 790 791 792 793 794 795
## 0.917330238 0.456648454 1.169745267 -1.097484484 -1.439886810 0.743072490
## 796 797 798 799 800 801
## -0.769316637 1.050760371 0.167335751 -0.474678047 -1.250353288 -0.495769852
## 802 803 804 805 806 807
## -0.033541569 -0.045704785 -0.676029505 0.327656022 0.147202408 -1.134045323
## 808 809 810 811 812 813
## -0.199226300 0.010968319 -0.375972155 -1.036052528 -0.167362222 0.034502523
## 814 815 816 817 818 819
## -0.783077379 -0.091445893 1.313945470 0.006014163 -1.113018235 0.401270507
## 820 822 823 824 827 828
## -1.342883667 0.973465378 0.481626212 0.195334652 -0.009310875 -0.367635715
## 829 830 831 832 833 834
## 1.182831111 -0.353343166 0.958729933 -1.532639174 1.863099071 0.546501182
## 835 836 837 839 840 841
## 0.841262289 2.397372444 2.965294852 1.085010108 2.747577729 0.114547029
## 842 844 846 849 850 851
## -0.378577435 -1.481731320 1.440371315 -1.289029823 0.666903448 -1.080778682
## 853 854 855 856 857 861
## 0.673095980 -0.991534337 -1.291624812 -1.622543424 -1.018666801 -0.986636327
## 862 863 864 865 866 867
## -0.397735441 -0.147241942 -0.027583590 -1.593988355 -0.770506874 -0.458217947
## 868 869 870 871 872 873
## -1.806826734 0.360151887 0.447148974 -1.688346237 -0.653481262 -1.075263598
## 875 876 877 879 880 881
## -1.025201392 -1.534931780 -0.439285640 0.071259400 -0.648546220 0.095994289
## 882 883 884 885 886 887
## -0.624418638 -0.884424343 -0.772154812 0.167150258 -0.259606537 0.772546963
## 888 889 890 891 892 893
## -0.306929062 -0.007899942 2.346748178 0.285568688 0.096120088 -0.634377322
## 894 895 896 897 898 899
## -0.709910974 0.564522238 0.150535386 -0.053921642 1.329802980 1.118796466
## 900 901 902 903 904 905
## 2.082943864 1.583481089 1.823814556 1.778661264 -0.652656736 -0.442153795
## 906 907 908 909 910 911
## 0.910387280 -0.776976749 -0.008543678 0.009148733 -1.042867706 -0.992993794
## 912 913 914 916 917 918
## -0.313405488 -1.097529200 -0.401780098 -0.742741955 -1.053811794 -1.414305826
## 920 921 922 923 924 925
## -0.548037889 0.006768294 -0.047312549 1.456611839 1.809885039 -0.576652884
## 926 927 928 929 930 931
## -0.465110643 -1.518703274 -1.493970704 0.381233011 -0.929556270 -0.389196775
## 932 933 934 935 936 937
## -0.974170129 -1.264746994 -0.561371032 -0.571049985 -1.728627463 2.494424882
## 938 939 940 941 942 943
## -0.745314466 0.316216707 -0.596245334 0.049400836 -0.369011522 -0.033149946
## 944 945 946 947 948 949
## -0.147100402 0.861759536 -0.208377058 -1.191498665 -1.009299180 -1.642646136
## 950 951 952 953 955 956
## -1.478556609 -0.646201637 0.699916653 -0.765566515 -1.404183508 -1.094391262
## 957 958 959 960 961 962
## -0.874107109 0.012174193 0.057602560 0.355313167 0.725902456 1.187825333
## 963 964 965 966 967 968
## 0.914428904 -0.181624104 -1.579245867 0.369868880 -0.786294392 -0.352712470
## 969 970 971 972 973 974
## -0.317975543 -1.096733525 -0.965810072 -1.146816553 1.832147962 0.040477056
## 975 976 977 979 980 981
## 0.115007272 0.471160445 -0.779013736 -0.322095907 0.154859380 -1.573008309
## 982 983 985 986 988 989
## 4.080215674 -0.511316062 1.566387568 4.584888713 -1.065703844 -0.357779633
## 990 991 992 993 994 995
## 3.449978725 -0.313601230 -1.691832009 -0.241299405 -0.627733459 2.579522597
## 996 997 998 999 1000 NA
## -0.539143186 -1.394381040 -0.125798970 -1.549099478 -0.622923767 NA
## NA.1 NA.2 NA.3 NA.4 NA.5 NA.6
## NA NA NA NA NA NA
## NA.7 NA.8 NA.9 NA.10 NA.11 NA.12
## NA NA NA NA NA NA
## NA.13 NA.14 NA.15 NA.16 NA.17 NA.18
## NA NA NA NA NA NA
## NA.19 NA.20 NA.21 NA.22 NA.23 NA.24
## NA NA NA NA NA NA
## NA.25 NA.26 NA.27 NA.28 NA.29 NA.30
## NA NA NA NA NA NA
## NA.31 NA.32 NA.33 NA.34 NA.35 NA.36
## NA NA NA NA NA NA
## NA.37 NA.38 NA.39 NA.40 NA.41 NA.42
## NA NA NA NA NA NA
## NA.43 NA.44 NA.45 NA.46 NA.47 NA.48
## NA NA NA NA NA NA
## NA.49 NA.50 NA.51 NA.52 NA.53 NA.54
## NA NA NA NA NA NA
## NA.55 NA.56 NA.57 NA.58 NA.59 NA.60
## NA NA NA NA NA NA
## NA.61 NA.62 NA.63 NA.64 NA.65 NA.66
## NA NA NA NA NA NA
## NA.67 NA.68 NA.69 NA.70 NA.71 NA.72
## NA NA NA NA NA NA
## NA.73 NA.74 NA.75 NA.76 NA.77 NA.78
## NA NA NA NA NA NA# Calculate performance metrics
n_full_r2 <- summary(n_full)$r.squared
model_test_r2 <- cor(n_test$Average.Flight.Fare, model2_pred)^2# Compare R-squared values
cat("Model 1 - Train R2:", n_full1_r2, "Test R2:", model1_test_r2, "\n")## Model 1 - Train R2: 0.983757 Test R2: 0.9780876cat("Model 2 - Train R2:", n_full_r2, "Test R2:", model_test_r2, "\n")## Model 2 - Train R2: 0.9732025 Test R2: NA# Load necessary libraries for ridge and lasso regression
library(glmnet)## Loading required package: Matrix## Loaded glmnet 4.1-8library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union###Ridge Regression
#Ridge Regression for Model 1
names(air1)## [1] "Average.Flight.Fare" "Distance"
## [3] "Average.Weekly.Passengers" "Market.Share.MLA"
## [5] "Average.MLA.Fare" "Market.Share.LPA"
## [7] "Average.LPA.Fare" "City1ABQ"
## [9] "City1ACY" "City1ALB"
## [11] "City1AMA" "City1ATL"
## [13] "City1AUS" "City1BDL"
## [15] "City1BHM" "City1BNA"
## [17] "City1BOI" "City1BOS"
## [19] "City1BTV" "City1BUF"
## [21] "City1BUR" "City1BWI"
## [23] "City1CAE" "City1CAK"
## [25] "City1CHS" "City1CLE"
## [27] "City1CLT" "City1CMH"
## [29] "City1COS" "City1CRP"
## [31] "City1CVG" "City1DAY"
## [33] "City1DEN" "City1DFW"
## [35] "City1DSM" "City1DTW"
## [37] "City1ELP" "City1EUG"
## [39] "City1FLL" "City1FNT"
## [41] "City1GPT" "City1GRR"
## [43] "City1GSO" "City1GSP"
## [45] "City1HRL" "City1IAD"
## [47] "City1IAH" "City1IND"
## [49] "City1ISP" "City1JAX"
## [51] "City1LAS" "City1LAX"
## [53] "City1LGA" "City1LGB"
## [55] "City1LIT" "City1MCI"
## [57] "City1MCO" "City1MDT"
## [59] "City1MEM" "City1MHT"
## [61] "City1MIA" "City1MKE"
## [63] "City1MSN" "City1MSP"
## [65] "City1MSY" "City1MYR"
## [67] "City1OAK" "City1OKC"
## [69] "City1OMA" "City1ONT"
## [71] "City1ORD" "City1ORF"
## [73] "City1PDX" "City1PHF"
## [75] "City1PHL" "City1PHX"
## [77] "City1PIT" "City1PSP"
## [79] "City1PVD" "City1RDU"
## [81] "City1RNO" "City1ROC"
## [83] "City1RSW" "City1SAN"
## [85] "City1SAT" "City1SDF"
## [87] "City1SEA" "City1SFO"
## [89] "City1SJC" "City1SLC"
## [91] "City1SMF" "City1SNA"
## [93] "City1STL" "City1SYR"
## [95] "City1TLH" "City1TPA"
## [97] "City1TYS" "City2ATL"
## [99] "City2AUS" "City2BDL"
## [101] "City2BHM" "City2BNA"
## [103] "City2BOS" "City2BUF"
## [105] "City2BWI" "City2CLE"
## [107] "City2CLT" "City2CMH"
## [109] "City2CVG" "City2DAY"
## [111] "City2DEN" "City2DFW"
## [113] "City2DSM" "City2DTW"
## [115] "City2ELP" "City2FLL"
## [117] "City2FNT" "City2GEG"
## [119] "City2GPT" "City2GSO"
## [121] "City2HPN" "City2HRL"
## [123] "City2IAD" "City2IAH"
## [125] "City2ICT" "City2IND"
## [127] "City2ISP" "City2JAN"
## [129] "City2JAX" "City2LAS"
## [131] "City2LAX" "City2LBB"
## [133] "City2LGA" "City2LGB"
## [135] "City2LIT" "City2MAF"
## [137] "City2MCI" "City2MCO"
## [139] "City2MDT" "City2MEM"
## [141] "City2MHT" "City2MIA"
## [143] "City2MKE" "City2MSP"
## [145] "City2MSY" "City2OAK"
## [147] "City2OKC" "City2OMA"
## [149] "City2ONT" "City2ORD"
## [151] "City2ORF" "City2PBI"
## [153] "City2PDX" "City2PHF"
## [155] "City2PHL" "City2PHX"
## [157] "City2PIT" "City2PNS"
## [159] "City2PVD" "City2PWM"
## [161] "City2RDU" "City2RIC"
## [163] "City2RNO" "City2ROC"
## [165] "City2RSW" "City2SAN"
## [167] "City2SAT" "City2SAV"
## [169] "City2SDF" "City2SEA"
## [171] "City2SFO" "City2SJC"
## [173] "City2SLC" "City2SMF"
## [175] "City2SNA" "City2SRQ"
## [177] "City2STL" "City2SYR"
## [179] "City2TLH" "City2TPA"
## [181] "City2TUL" "City2TUS"
## [183] "Market.Leading.Airline..MLA.AA" "Market.Leading.Airline..MLA.AS"
## [185] "Market.Leading.Airline..MLA.B6" "Market.Leading.Airline..MLA.CO"
## [187] "Market.Leading.Airline..MLA.DL" "Market.Leading.Airline..MLA.F9"
## [189] "Market.Leading.Airline..MLA.FL" "Market.Leading.Airline..MLA.G4"
## [191] "Market.Leading.Airline..MLA.HP" "Market.Leading.Airline..MLA.NK"
## [193] "Market.Leading.Airline..MLA.NW" "Market.Leading.Airline..MLA.TZ"
## [195] "Market.Leading.Airline..MLA.UA" "Market.Leading.Airline..MLA.US"
## [197] "Market.Leading.Airline..MLA.WN" "Market.Leading.Airline..MLA.YX"
## [199] "Low.Price.Airline..LPA.AA" "Low.Price.Airline..LPA.AQ"
## [201] "Low.Price.Airline..LPA.AS" "Low.Price.Airline..LPA.AX"
## [203] "Low.Price.Airline..LPA.B6" "Low.Price.Airline..LPA.CO"
## [205] "Low.Price.Airline..LPA.DL" "Low.Price.Airline..LPA.F9"
## [207] "Low.Price.Airline..LPA.FL" "Low.Price.Airline..LPA.G4"
## [209] "Low.Price.Airline..LPA.HP" "Low.Price.Airline..LPA.NK"
## [211] "Low.Price.Airline..LPA.NW" "Low.Price.Airline..LPA.SY"
## [213] "Low.Price.Airline..LPA.TZ" "Low.Price.Airline..LPA.UA"
## [215] "Low.Price.Airline..LPA.US" "Low.Price.Airline..LPA.WN"
## [217] "Low.Price.Airline..LPA.YX"# View the first few rows of the dataset
head(air1)## Average.Flight.Fare Distance Average.Weekly.Passengers Market.Share.MLA
## 1 -0.88331987 -0.8224041 -0.32317406 0.5194922
## 2 -0.73882548 -0.3062376 -0.51588938 0.7730061
## 3 0.92195695 -0.3186753 -0.59557426 0.9686919
## 4 -1.69736515 -1.1955366 -0.08537182 1.9022011
## 5 -0.09474172 -0.5192340 -0.46866290 -1.0501251
## 6 -0.50944063 0.2285856 -0.61741325 -1.0041725
## Average.MLA.Fare Market.Share.LPA Average.LPA.Fare City1ABQ City1ACY
## 1 -0.88760670 1.3152202 -0.68100033 -0.1353204 -0.06334073
## 2 -0.69513984 -0.6392398 -0.51351914 -0.1353204 -0.06334073
## 3 0.91497465 -1.1728782 0.50661281 -0.1353204 -0.06334073
## 4 -1.56060232 2.3035215 -1.57388045 -0.1353204 -0.06334073
## 5 -0.08438475 -0.7089892 0.04715114 -0.1353204 -0.06334073
## 6 -0.45766830 -0.6444064 -0.32825233 -0.1353204 -0.06334073
## City1ALB City1AMA City1ATL City1AUS City1BDL City1BHM
## 1 -0.08975774 -0.04474376 -0.2613574 -0.1463865 -0.1190991 -0.08975774
## 2 -0.08975774 -0.04474376 -0.2613574 -0.1463865 -0.1190991 -0.08975774
## 3 11.12995957 -0.04474376 -0.2613574 -0.1463865 -0.1190991 -0.08975774
## 4 11.12995957 -0.04474376 -0.2613574 -0.1463865 -0.1190991 -0.08975774
## 5 11.12995957 -0.04474376 -0.2613574 -0.1463865 -0.1190991 -0.08975774
## 6 11.12995957 -0.04474376 -0.2613574 -0.1463865 -0.1190991 -0.08975774
## City1BNA City1BOI City1BOS City1BTV City1BUF City1BUR
## 1 -0.1054098 -0.08975774 -0.1986493 -0.03162278 -0.09525048 -0.1004535
## 2 -0.1054098 -0.08975774 -0.1986493 -0.03162278 -0.09525048 -0.1004535
## 3 -0.1054098 -0.08975774 -0.1986493 -0.03162278 -0.09525048 -0.1004535
## 4 -0.1054098 -0.08975774 -0.1986493 -0.03162278 -0.09525048 -0.1004535
## 5 -0.1054098 -0.08975774 -0.1986493 -0.03162278 -0.09525048 -0.1004535
## 6 -0.1054098 -0.08975774 -0.1986493 -0.03162278 -0.09525048 -0.1004535
## City1BWI City1CAE City1CAK City1CHS City1CLE City1CLT
## 1 -0.2219654 -0.03162278 22.32713596 -0.03162278 -0.1499079 -0.1353204
## 2 -0.2219654 -0.03162278 22.32713596 -0.03162278 -0.1499079 -0.1353204
## 3 -0.2219654 -0.03162278 -0.04474376 -0.03162278 -0.1499079 -0.1353204
## 4 -0.2219654 -0.03162278 -0.04474376 -0.03162278 -0.1499079 -0.1353204
## 5 -0.2219654 -0.03162278 -0.04474376 -0.03162278 -0.1499079 -0.1353204
## 6 -0.2219654 -0.03162278 -0.04474376 -0.03162278 -0.1499079 -0.1353204
## City1CMH City1COS City1CRP City1CVG City1DAY City1DEN
## 1 -0.1353204 -0.05482717 -0.04474376 -0.1101526 -0.05482717 -0.1931503
## 2 -0.1353204 -0.05482717 -0.04474376 -0.1101526 -0.05482717 -0.1931503
## 3 -0.1353204 -0.05482717 -0.04474376 -0.1101526 -0.05482717 -0.1931503
## 4 -0.1353204 -0.05482717 -0.04474376 -0.1101526 -0.05482717 -0.1931503
## 5 -0.1353204 -0.05482717 -0.04474376 -0.1101526 -0.05482717 -0.1931503
## 6 -0.1353204 -0.05482717 -0.04474376 -0.1101526 -0.05482717 -0.1931503
## City1DFW City1DSM City1DTW City1ELP City1EUG City1FLL
## 1 -0.2340886 -0.04474376 -0.1846403 -0.07765424 -0.03162278 -0.1727317
## 2 -0.2340886 -0.04474376 -0.1846403 -0.07765424 -0.03162278 -0.1727317
## 3 -0.2340886 -0.04474376 -0.1846403 -0.07765424 -0.03162278 -0.1727317
## 4 -0.2340886 -0.04474376 -0.1846403 -0.07765424 -0.03162278 -0.1727317
## 5 -0.2340886 -0.04474376 -0.1846403 -0.07765424 -0.03162278 -0.1727317
## 6 -0.2340886 -0.04474376 -0.1846403 -0.07765424 -0.03162278 -0.1727317
## City1FNT City1GPT City1GRR City1GSO City1GSP City1HRL
## 1 -0.03162278 -0.03162278 -0.06334073 -0.05482717 -0.03162278 -0.03162278
## 2 -0.03162278 -0.03162278 -0.06334073 -0.05482717 -0.03162278 -0.03162278
## 3 -0.03162278 -0.03162278 -0.06334073 -0.05482717 -0.03162278 -0.03162278
## 4 -0.03162278 -0.03162278 -0.06334073 -0.05482717 -0.03162278 -0.03162278
## 5 -0.03162278 -0.03162278 -0.06334073 -0.05482717 -0.03162278 -0.03162278
## 6 -0.03162278 -0.03162278 -0.06334073 -0.05482717 -0.03162278 -0.03162278
## City1IAD City1IAH City1IND City1ISP City1JAX City1LAS
## 1 -0.03162278 -0.1986493 -0.1101526 -0.07085267 -0.08391839 -0.1959163
## 2 -0.03162278 -0.1986493 -0.1101526 -0.07085267 -0.08391839 -0.1959163
## 3 -0.03162278 -0.1986493 -0.1101526 -0.07085267 -0.08391839 -0.1959163
## 4 -0.03162278 -0.1986493 -0.1101526 -0.07085267 -0.08391839 -0.1959163
## 5 -0.03162278 -0.1986493 -0.1101526 -0.07085267 -0.08391839 -0.1959163
## 6 -0.03162278 -0.1986493 -0.1101526 -0.07085267 -0.08391839 -0.1959163
## City1LAX City1LGA City1LGB City1LIT City1MCI City1MCO
## 1 -0.1787729 -0.1696401 -0.06334073 -0.03162278 -0.1533555 -0.1314409
## 2 -0.1787729 -0.1696401 -0.06334073 -0.03162278 -0.1533555 -0.1314409
## 3 -0.1787729 -0.1696401 -0.06334073 -0.03162278 -0.1533555 -0.1314409
## 4 -0.1787729 -0.1696401 -0.06334073 -0.03162278 -0.1533555 -0.1314409
## 5 -0.1787729 -0.1696401 -0.06334073 -0.03162278 -0.1533555 -0.1314409
## 6 -0.1787729 -0.1696401 -0.06334073 -0.03162278 -0.1533555 -0.1314409
## City1MDT City1MEM City1MHT City1MIA City1MKE City1MSN
## 1 -0.03162278 -0.07765424 -0.05482717 -0.1147086 -0.08391839 -0.03162278
## 2 -0.03162278 -0.07765424 -0.05482717 -0.1147086 -0.08391839 -0.03162278
## 3 -0.03162278 -0.07765424 -0.05482717 -0.1147086 -0.08391839 -0.03162278
## 4 -0.03162278 -0.07765424 -0.05482717 -0.1147086 -0.08391839 -0.03162278
## 5 -0.03162278 -0.07765424 -0.05482717 -0.1147086 -0.08391839 -0.03162278
## 6 -0.03162278 -0.07765424 -0.05482717 -0.1147086 -0.08391839 -0.03162278
## City1MSP City1MSY City1MYR City1OAK City1OKC City1OMA
## 1 -0.1427857 -0.1233418 -0.03162278 -0.1101526 -0.04474376 -0.06334073
## 2 -0.1427857 -0.1233418 -0.03162278 -0.1101526 -0.04474376 -0.06334073
## 3 -0.1427857 -0.1233418 -0.03162278 -0.1101526 -0.04474376 -0.06334073
## 4 -0.1427857 -0.1233418 -0.03162278 -0.1101526 -0.04474376 -0.06334073
## 5 -0.1427857 -0.1233418 -0.03162278 -0.1101526 -0.04474376 -0.06334073
## 6 -0.1427857 -0.1233418 -0.03162278 -0.1101526 -0.04474376 -0.06334073
## City1ONT City1ORD City1ORF City1PDX City1PHF City1PHL
## 1 -0.08975774 -0.2591691 -0.05482717 -0.1054098 -0.03162278 -0.1054098
## 2 -0.08975774 -0.2591691 -0.05482717 -0.1054098 -0.03162278 -0.1054098
## 3 -0.08975774 -0.2591691 -0.05482717 -0.1054098 -0.03162278 -0.1054098
## 4 -0.08975774 -0.2591691 -0.05482717 -0.1054098 -0.03162278 -0.1054098
## 5 -0.08975774 -0.2591691 -0.05482717 -0.1054098 -0.03162278 -0.1054098
## 6 -0.08975774 -0.2591691 -0.05482717 -0.1054098 -0.03162278 -0.1054098
## City1PHX City1PIT City1PSP City1PVD City1RDU City1RNO
## 1 -0.1353204 -0.05482717 -0.05482717 -0.07085267 -0.07085267 -0.05482717
## 2 -0.1353204 -0.05482717 -0.05482717 -0.07085267 -0.07085267 -0.05482717
## 3 -0.1353204 -0.05482717 -0.05482717 -0.07085267 -0.07085267 -0.05482717
## 4 -0.1353204 -0.05482717 -0.05482717 -0.07085267 -0.07085267 -0.05482717
## 5 -0.1353204 -0.05482717 -0.05482717 -0.07085267 -0.07085267 -0.05482717
## 6 -0.1353204 -0.05482717 -0.05482717 -0.07085267 -0.07085267 -0.05482717
## City1ROC City1RSW City1SAN City1SAT City1SDF City1SEA
## 1 -0.03162278 -0.08975774 -0.07765424 -0.05482717 -0.06334073 -0.06334073
## 2 -0.03162278 -0.08975774 -0.07765424 -0.05482717 -0.06334073 -0.06334073
## 3 -0.03162278 -0.08975774 -0.07765424 -0.05482717 -0.06334073 -0.06334073
## 4 -0.03162278 -0.08975774 -0.07765424 -0.05482717 -0.06334073 -0.06334073
## 5 -0.03162278 -0.08975774 -0.07765424 -0.05482717 -0.06334073 -0.06334073
## 6 -0.03162278 -0.08975774 -0.07765424 -0.05482717 -0.06334073 -0.06334073
## City1SFO City1SJC City1SLC City1SMF City1SNA City1STL
## 1 -0.06334073 -0.06334073 -0.08391839 -0.07765424 -0.03162278 -0.09525048
## 2 -0.06334073 -0.06334073 -0.08391839 -0.07765424 -0.03162278 -0.09525048
## 3 -0.06334073 -0.06334073 -0.08391839 -0.07765424 -0.03162278 -0.09525048
## 4 -0.06334073 -0.06334073 -0.08391839 -0.07765424 -0.03162278 -0.09525048
## 5 -0.06334073 -0.06334073 -0.08391839 -0.07765424 -0.03162278 -0.09525048
## 6 -0.06334073 -0.06334073 -0.08391839 -0.07765424 -0.03162278 -0.09525048
## City1SYR City1TLH City1TPA City1TYS City2ATL City2AUS
## 1 -0.03162278 -0.03162278 -0.04474376 -0.03162278 18.22089460 -0.03162278
## 2 -0.03162278 -0.03162278 -0.04474376 -0.03162278 -0.05482717 -0.03162278
## 3 -0.03162278 -0.03162278 -0.04474376 -0.03162278 18.22089460 -0.03162278
## 4 -0.03162278 -0.03162278 -0.04474376 -0.03162278 -0.05482717 -0.03162278
## 5 -0.03162278 -0.03162278 -0.04474376 -0.03162278 -0.05482717 -0.03162278
## 6 -0.03162278 -0.03162278 -0.04474376 -0.03162278 -0.05482717 -0.03162278
## City2BDL City2BHM City2BNA City2BOS City2BUF City2BWI
## 1 -0.1004535 -0.03162278 -0.1274516 -0.04474376 -0.04474376 -0.06334073
## 2 -0.1004535 -0.03162278 -0.1274516 -0.04474376 -0.04474376 -0.06334073
## 3 -0.1004535 -0.03162278 -0.1274516 -0.04474376 -0.04474376 -0.06334073
## 4 -0.1004535 -0.03162278 -0.1274516 -0.04474376 -0.04474376 15.77184200
## 5 -0.1004535 -0.03162278 -0.1274516 -0.04474376 -0.04474376 -0.06334073
## 6 -0.1004535 -0.03162278 -0.1274516 -0.04474376 -0.04474376 -0.06334073
## City2CLE City2CLT City2CMH City2CVG City2DAY City2DEN
## 1 -0.06334073 -0.05482717 -0.06334073 -0.05482717 -0.06334073 -0.1101526
## 2 -0.06334073 -0.05482717 -0.06334073 -0.05482717 -0.06334073 -0.1101526
## 3 -0.06334073 -0.05482717 -0.06334073 -0.05482717 -0.06334073 -0.1101526
## 4 -0.06334073 -0.05482717 -0.06334073 -0.05482717 -0.06334073 -0.1101526
## 5 -0.06334073 -0.05482717 -0.06334073 -0.05482717 -0.06334073 -0.1101526
## 6 -0.06334073 -0.05482717 -0.06334073 -0.05482717 -0.06334073 -0.1101526
## City2DFW City2DSM City2DTW City2ELP City2FLL City2FNT
## 1 -0.1274516 -0.03162278 -0.08391839 -0.05482717 -0.1190991 -0.03162278
## 2 -0.1274516 -0.03162278 -0.08391839 -0.05482717 -0.1190991 -0.03162278
## 3 -0.1274516 -0.03162278 -0.08391839 -0.05482717 -0.1190991 -0.03162278
## 4 -0.1274516 -0.03162278 -0.08391839 -0.05482717 -0.1190991 -0.03162278
## 5 -0.1274516 -0.03162278 -0.08391839 -0.05482717 -0.1190991 -0.03162278
## 6 -0.1274516 -0.03162278 -0.08391839 -0.05482717 8.3879761 -0.03162278
## City2GEG City2GPT City2GSO City2HPN City2HRL City2IAD
## 1 -0.09525048 -0.03162278 -0.05482717 -0.03162278 -0.03162278 -0.2040221
## 2 -0.09525048 -0.03162278 -0.05482717 -0.03162278 -0.03162278 -0.2040221
## 3 -0.09525048 -0.03162278 -0.05482717 -0.03162278 -0.03162278 -0.2040221
## 4 -0.09525048 -0.03162278 -0.05482717 -0.03162278 -0.03162278 -0.2040221
## 5 -0.09525048 -0.03162278 -0.05482717 -0.03162278 -0.03162278 -0.2040221
## 6 -0.09525048 -0.03162278 -0.05482717 -0.03162278 -0.03162278 -0.2040221
## City2IAH City2ICT City2IND City2ISP City2JAN City2JAX
## 1 -0.1427857 -0.03162278 -0.09525048 -0.05482717 -0.04474376 -0.08975774
## 2 -0.1427857 -0.03162278 -0.09525048 -0.05482717 -0.04474376 -0.08975774
## 3 -0.1427857 -0.03162278 -0.09525048 -0.05482717 -0.04474376 -0.08975774
## 4 -0.1427857 -0.03162278 -0.09525048 -0.05482717 -0.04474376 -0.08975774
## 5 -0.1427857 -0.03162278 -0.09525048 -0.05482717 -0.04474376 -0.08975774
## 6 -0.1427857 -0.03162278 -0.09525048 -0.05482717 -0.04474376 -0.08975774
## City2LAS City2LAX City2LBB City2LGA City2LGB City2LIT
## 1 -0.1633014 -0.1664977 -0.05482717 -0.2092785 -0.04474376 -0.05482717
## 2 -0.1633014 -0.1664977 -0.05482717 -0.2092785 -0.04474376 -0.05482717
## 3 -0.1633014 -0.1664977 -0.05482717 -0.2092785 -0.04474376 -0.05482717
## 4 -0.1633014 -0.1664977 -0.05482717 -0.2092785 -0.04474376 -0.05482717
## 5 -0.1633014 -0.1664977 -0.05482717 -0.2092785 -0.04474376 -0.05482717
## 6 -0.1633014 -0.1664977 -0.05482717 -0.2092785 -0.04474376 -0.05482717
## City2MAF City2MCI City2MCO City2MDT City2MEM City2MHT
## 1 -0.04474376 -0.1054098 -0.2118659 -0.03162278 -0.08391839 -0.07085267
## 2 -0.04474376 -0.1054098 4.7152474 -0.03162278 -0.08391839 -0.07085267
## 3 -0.04474376 -0.1054098 -0.2118659 -0.03162278 -0.08391839 -0.07085267
## 4 -0.04474376 -0.1054098 -0.2118659 -0.03162278 -0.08391839 -0.07085267
## 5 -0.04474376 -0.1054098 -0.2118659 -0.03162278 -0.08391839 -0.07085267
## 6 -0.04474376 -0.1054098 -0.2118659 -0.03162278 -0.08391839 -0.07085267
## City2MIA City2MKE City2MSP City2MSY City2OAK City2OKC City2OMA
## 1 -0.1101526 -0.1004535 -0.1600481 -0.1390993 -0.1274516 -0.1004535 -0.08975774
## 2 -0.1101526 -0.1004535 -0.1600481 -0.1390993 -0.1274516 -0.1004535 -0.08975774
## 3 -0.1101526 -0.1004535 -0.1600481 -0.1390993 -0.1274516 -0.1004535 -0.08975774
## 4 -0.1101526 -0.1004535 -0.1600481 -0.1390993 -0.1274516 -0.1004535 -0.08975774
## 5 -0.1101526 -0.1004535 -0.1600481 -0.1390993 -0.1274516 -0.1004535 -0.08975774
## 6 -0.1101526 -0.1004535 -0.1600481 -0.1390993 -0.1274516 -0.1004535 -0.08975774
## City2ONT City2ORD City2ORF City2PBI City2PDX City2PHF
## 1 -0.1004535 -0.09525048 -0.08975774 -0.1190991 -0.1499079 -0.03162278
## 2 -0.1004535 -0.09525048 -0.08975774 -0.1190991 -0.1499079 -0.03162278
## 3 -0.1004535 -0.09525048 -0.08975774 -0.1190991 -0.1499079 -0.03162278
## 4 -0.1004535 -0.09525048 -0.08975774 -0.1190991 -0.1499079 -0.03162278
## 5 -0.1004535 10.48813615 -0.08975774 -0.1190991 -0.1499079 -0.03162278
## 6 -0.1004535 -0.09525048 -0.08975774 -0.1190991 -0.1499079 -0.03162278
## City2PHL City2PHX City2PIT City2PNS City2PVD City2PWM
## 1 -0.1533555 -0.1931503 -0.1314409 -0.03162278 -0.1101526 -0.03162278
## 2 -0.1533555 -0.1931503 -0.1314409 -0.03162278 -0.1101526 -0.03162278
## 3 -0.1533555 -0.1931503 -0.1314409 -0.03162278 -0.1101526 -0.03162278
## 4 -0.1533555 -0.1931503 -0.1314409 -0.03162278 -0.1101526 -0.03162278
## 5 -0.1533555 -0.1931503 -0.1314409 -0.03162278 -0.1101526 -0.03162278
## 6 -0.1533555 -0.1931503 -0.1314409 -0.03162278 -0.1101526 -0.03162278
## City2RDU City2RIC City2RNO City2ROC City2RSW City2SAN
## 1 -0.1533555 -0.06334073 -0.09525048 -0.07085267 -0.09525048 -0.1931503
## 2 -0.1533555 -0.06334073 -0.09525048 -0.07085267 -0.09525048 -0.1931503
## 3 -0.1533555 -0.06334073 -0.09525048 -0.07085267 -0.09525048 -0.1931503
## 4 -0.1533555 -0.06334073 -0.09525048 -0.07085267 -0.09525048 -0.1931503
## 5 -0.1533555 -0.06334073 -0.09525048 -0.07085267 -0.09525048 -0.1931503
## 6 -0.1533555 -0.06334073 -0.09525048 -0.07085267 -0.09525048 -0.1931503
## City2SAT City2SAV City2SDF City2SEA City2SFO City2SJC
## 1 -0.1353204 -0.04474376 -0.07765424 -0.2092785 -0.1787729 -0.1499079
## 2 -0.1353204 -0.04474376 -0.07765424 -0.2092785 -0.1787729 -0.1499079
## 3 -0.1353204 -0.04474376 -0.07765424 -0.2092785 -0.1787729 -0.1499079
## 4 -0.1353204 -0.04474376 -0.07765424 -0.2092785 -0.1787729 -0.1499079
## 5 -0.1353204 -0.04474376 -0.07765424 -0.2092785 -0.1787729 -0.1499079
## 6 -0.1353204 -0.04474376 -0.07765424 -0.2092785 -0.1787729 -0.1499079
## City2SLC City2SMF City2SNA City2SRQ City2STL City2SYR
## 1 -0.1427857 -0.1314409 -0.1390993 -0.04474376 -0.1787729 -0.05482717
## 2 -0.1427857 -0.1314409 -0.1390993 -0.04474376 -0.1787729 -0.05482717
## 3 -0.1427857 -0.1314409 -0.1390993 -0.04474376 -0.1787729 -0.05482717
## 4 -0.1427857 -0.1314409 -0.1390993 -0.04474376 -0.1787729 -0.05482717
## 5 -0.1427857 -0.1314409 -0.1390993 -0.04474376 -0.1787729 -0.05482717
## 6 -0.1427857 -0.1314409 -0.1390993 -0.04474376 -0.1787729 -0.05482717
## City2TLH City2TPA City2TUL City2TUS Market.Leading.Airline..MLA.AA
## 1 -0.04474376 -0.2194762 -0.07765424 -0.1101526 -0.3948586
## 2 -0.04474376 -0.2194762 -0.07765424 -0.1101526 -0.3948586
## 3 -0.04474376 -0.2194762 -0.07765424 -0.1101526 -0.3948586
## 4 -0.04474376 -0.2194762 -0.07765424 -0.1101526 -0.3948586
## 5 -0.04474376 -0.2194762 -0.07765424 -0.1101526 -0.3948586
## 6 -0.04474376 -0.2194762 -0.07765424 -0.1101526 -0.3948586
## Market.Leading.Airline..MLA.AS Market.Leading.Airline..MLA.B6
## 1 -0.1817272 -0.1190991
## 2 -0.1817272 -0.1190991
## 3 -0.1817272 -0.1190991
## 4 -0.1817272 -0.1190991
## 5 -0.1817272 -0.1190991
## 6 -0.1817272 -0.1190991
## Market.Leading.Airline..MLA.CO Market.Leading.Airline..MLA.DL
## 1 -0.2721024 -0.4394589
## 2 -0.2721024 -0.4394589
## 3 -0.2721024 2.2732502
## 4 -0.2721024 -0.4394589
## 5 -0.2721024 -0.4394589
## 6 -0.2721024 -0.4394589
## Market.Leading.Airline..MLA.F9 Market.Leading.Airline..MLA.FL
## 1 -0.05482717 7.8382715
## 2 -0.05482717 7.8382715
## 3 -0.05482717 -0.1274516
## 4 -0.05482717 -0.1274516
## 5 -0.05482717 -0.1274516
## 6 -0.05482717 -0.1274516
## Market.Leading.Airline..MLA.G4 Market.Leading.Airline..MLA.HP
## 1 -0.03162278 -0.1757752
## 2 -0.03162278 -0.1757752
## 3 -0.03162278 -0.1757752
## 4 -0.03162278 -0.1757752
## 5 -0.03162278 -0.1757752
## 6 -0.03162278 -0.1757752
## Market.Leading.Airline..MLA.NK Market.Leading.Airline..MLA.NW
## 1 -0.1004535 -0.3065906
## 2 -0.1004535 -0.3065906
## 3 -0.1004535 -0.3065906
## 4 -0.1004535 -0.3065906
## 5 -0.1004535 -0.3065906
## 6 -0.1004535 -0.3065906
## Market.Leading.Airline..MLA.TZ Market.Leading.Airline..MLA.UA
## 1 -0.1004535 -0.3162434
## 2 -0.1004535 -0.3162434
## 3 -0.1004535 -0.3162434
## 4 -0.1004535 -0.3162434
## 5 -0.1004535 3.1589590
## 6 -0.1004535 -0.3162434
## Market.Leading.Airline..MLA.US Market.Leading.Airline..MLA.WN
## 1 -0.2699785 -0.5878343
## 2 -0.2699785 -0.5878343
## 3 -0.2699785 -0.5878343
## 4 -0.2699785 1.6994586
## 5 -0.2699785 -0.5878343
## 6 -0.2699785 1.6994586
## Market.Leading.Airline..MLA.YX Low.Price.Airline..LPA.AA
## 1 -0.1274516 -0.3846525
## 2 -0.1274516 -0.3846525
## 3 -0.1274516 -0.3846525
## 4 -0.1274516 -0.3846525
## 5 -0.1274516 -0.3846525
## 6 -0.1274516 -0.3846525
## Low.Price.Airline..LPA.AQ Low.Price.Airline..LPA.AS Low.Price.Airline..LPA.AX
## 1 -0.04474376 -0.1664977 -0.05482717
## 2 -0.04474376 -0.1664977 -0.05482717
## 3 -0.04474376 -0.1664977 -0.05482717
## 4 -0.04474376 -0.1664977 -0.05482717
## 5 -0.04474376 -0.1664977 -0.05482717
## 6 -0.04474376 -0.1664977 -0.05482717
## Low.Price.Airline..LPA.B6 Low.Price.Airline..LPA.CO Low.Price.Airline..LPA.DL
## 1 -0.1233418 -0.2169638 -0.4215192
## 2 -0.1233418 -0.2169638 2.3699987
## 3 -0.1233418 4.6044544 -0.4215192
## 4 -0.1233418 -0.2169638 -0.4215192
## 5 -0.1233418 -0.2169638 -0.4215192
## 6 -0.1233418 -0.2169638 2.3699987
## Low.Price.Airline..LPA.F9 Low.Price.Airline..LPA.FL Low.Price.Airline..LPA.G4
## 1 -0.1757752 3.9561345 -0.03162278
## 2 -0.1757752 -0.2525192 -0.03162278
## 3 -0.1757752 -0.2525192 -0.03162278
## 4 -0.1757752 -0.2525192 -0.03162278
## 5 -0.1757752 -0.2525192 -0.03162278
## 6 -0.1757752 -0.2525192 -0.03162278
## Low.Price.Airline..LPA.HP Low.Price.Airline..LPA.NK Low.Price.Airline..LPA.NW
## 1 -0.2480112 -0.1274516 -0.1846403
## 2 -0.2480112 -0.1274516 -0.1846403
## 3 -0.2480112 -0.1274516 -0.1846403
## 4 -0.2480112 -0.1274516 -0.1846403
## 5 -0.2480112 -0.1274516 -0.1846403
## 6 -0.2480112 -0.1274516 -0.1846403
## Low.Price.Airline..LPA.SY Low.Price.Airline..LPA.TZ Low.Price.Airline..LPA.UA
## 1 -0.08391839 -0.187514 -0.2411283
## 2 -0.08391839 -0.187514 -0.2411283
## 3 -0.08391839 -0.187514 -0.2411283
## 4 -0.08391839 -0.187514 -0.2411283
## 5 -0.08391839 -0.187514 -0.2411283
## 6 -0.08391839 -0.187514 -0.2411283
## Low.Price.Airline..LPA.US Low.Price.Airline..LPA.WN Low.Price.Airline..LPA.YX
## 1 -0.3350145 -0.5462624 -0.05482717
## 2 -0.3350145 -0.5462624 -0.05482717
## 3 -0.3350145 -0.5462624 -0.05482717
## 4 -0.3350145 1.8287915 -0.05482717
## 5 -0.3350145 1.8287915 -0.05482717
## 6 -0.3350145 -0.5462624 -0.05482717# Create model matrix
X <- model.matrix(Average.Flight.Fare ~ ., air1)
# Separate the target variable
Y <- air1$Average.Flight.Fare
# Define the lambda sequence
lambda <- 10^seq(10, -2, length = 100)
print(lambda)## [1] 1.000000e+10 7.564633e+09 5.722368e+09 4.328761e+09 3.274549e+09
## [6] 2.477076e+09 1.873817e+09 1.417474e+09 1.072267e+09 8.111308e+08
## [11] 6.135907e+08 4.641589e+08 3.511192e+08 2.656088e+08 2.009233e+08
## [16] 1.519911e+08 1.149757e+08 8.697490e+07 6.579332e+07 4.977024e+07
## [21] 3.764936e+07 2.848036e+07 2.154435e+07 1.629751e+07 1.232847e+07
## [26] 9.326033e+06 7.054802e+06 5.336699e+06 4.037017e+06 3.053856e+06
## [31] 2.310130e+06 1.747528e+06 1.321941e+06 1.000000e+06 7.564633e+05
## [36] 5.722368e+05 4.328761e+05 3.274549e+05 2.477076e+05 1.873817e+05
## [41] 1.417474e+05 1.072267e+05 8.111308e+04 6.135907e+04 4.641589e+04
## [46] 3.511192e+04 2.656088e+04 2.009233e+04 1.519911e+04 1.149757e+04
## [51] 8.697490e+03 6.579332e+03 4.977024e+03 3.764936e+03 2.848036e+03
## [56] 2.154435e+03 1.629751e+03 1.232847e+03 9.326033e+02 7.054802e+02
## [61] 5.336699e+02 4.037017e+02 3.053856e+02 2.310130e+02 1.747528e+02
## [66] 1.321941e+02 1.000000e+02 7.564633e+01 5.722368e+01 4.328761e+01
## [71] 3.274549e+01 2.477076e+01 1.873817e+01 1.417474e+01 1.072267e+01
## [76] 8.111308e+00 6.135907e+00 4.641589e+00 3.511192e+00 2.656088e+00
## [81] 2.009233e+00 1.519911e+00 1.149757e+00 8.697490e-01 6.579332e-01
## [86] 4.977024e-01 3.764936e-01 2.848036e-01 2.154435e-01 1.629751e-01
## [91] 1.232847e-01 9.326033e-02 7.054802e-02 5.336699e-02 4.037017e-02
## [96] 3.053856e-02 2.310130e-02 1.747528e-02 1.321941e-02 1.000000e-02# Split the data into training and validation sets
set.seed(123)
part <- sample(2, nrow(X), replace = TRUE, prob = c(0.7, 0.3))
X_train <- X[part == 1, ]
X_cv <- X[part == 2, ]
Y_train <- Y[part == 1]
Y_cv <- Y[part == 2]
# Perform Ridge regression
ridge_reg <- glmnet(X_train, Y_train, alpha = 0, lambda = lambda)
summary(ridge_reg)## Length Class Mode
## a0 100 -none- numeric
## beta 21700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric# Find the best lambda via cross-validation
ridge_reg1 <- cv.glmnet(X_train, Y_train, alpha = 0)
bestlam <- ridge_reg1$lambda.min
print(bestlam)## [1] 0.09790607# Predict on the validation set
ridge.pred <- predict(ridge_reg, s = bestlam, newx = X_cv)
# Calculate mean squared error
mse <- mean((Y_cv - ridge.pred)^2)
print(paste("Mean Squared Error:", mse))## [1] "Mean Squared Error: 0.0382175241817077"# Calculate R2 value
sst <- sum((Y_cv - mean(Y_cv))^2)
sse <- sum((Y_cv - ridge.pred)^2)
r2 <- 1 - (sse / sst)
print(paste("R²:", r2))## [1] "R²: 0.962440339230364"# Get the Ridge regression coefficients
ridge.coef <- predict(ridge_reg, type = "coefficients", s = bestlam)
print("Ridge Coefficients:")## [1] "Ridge Coefficients:"print(ridge.coef)## 218 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 0.0048235849
## (Intercept) .
## Distance 0.1099495731
## Average.Weekly.Passengers -0.0387842198
## Market.Share.MLA 0.0531392501
## Average.MLA.Fare 0.5076811579
## Market.Share.LPA -0.0726859227
## Average.LPA.Fare 0.2406231801
## City1ABQ -0.0038716764
## City1ACY -0.0073428897
## City1ALB -0.0028684204
## City1AMA -0.0095332456
## City1ATL 0.0175874744
## City1AUS 0.0031069112
## City1BDL -0.0052307025
## City1BHM 0.0013054701
## City1BNA 0.0018342874
## City1BOI -0.0105164506
## City1BOS 0.0170169071
## City1BTV .
## City1BUF -0.0141411029
## City1BUR -0.0123189262
## City1BWI -0.0070126773
## City1CAE .
## City1CAK -0.0031260034
## City1CHS -0.0030350228
## City1CLE 0.0104575743
## City1CLT 0.0118211055
## City1CMH -0.0207155589
## City1COS -0.0059245493
## City1CRP -0.0041879216
## City1CVG 0.0272323065
## City1DAY -0.0046936652
## City1DEN 0.0071671768
## City1DFW 0.0241111476
## City1DSM -0.0024849716
## City1DTW 0.0134629733
## City1ELP -0.0088225601
## City1EUG .
## City1FLL -0.0234146506
## City1FNT -0.0063996173
## City1GPT -0.0011667592
## City1GRR -0.0136267415
## City1GSO -0.0077809285
## City1GSP 0.0033419213
## City1HRL -0.0076285360
## City1IAD -0.0041590817
## City1IAH 0.0247704606
## City1IND -0.0174930385
## City1ISP -0.0160863172
## City1JAX -0.0115237111
## City1LAS -0.0173187302
## City1LAX 0.0128206520
## City1LGA 0.0390348463
## City1LGB -0.0184528348
## City1LIT -0.0067685441
## City1MCI -0.0027859711
## City1MCO -0.0129219064
## City1MDT .
## City1MEM 0.0059007028
## City1MHT -0.0058560638
## City1MIA 0.0011219309
## City1MKE -0.0053139363
## City1MSN -0.0071346328
## City1MSP 0.0123770482
## City1MSY -0.0133232562
## City1MYR -0.0096275608
## City1OAK -0.0060367666
## City1OKC -0.0008536968
## City1OMA -0.0063710507
## City1ONT -0.0080729050
## City1ORD 0.0110057724
## City1ORF -0.0013921104
## City1PDX -0.0135806920
## City1PHF -0.0081222795
## City1PHL 0.0201477341
## City1PHX -0.0004554018
## City1PIT -0.0034505199
## City1PSP -0.0134043618
## City1PVD -0.0073325409
## City1RDU -0.0047554016
## City1RNO -0.0120029508
## City1ROC -0.0046729804
## City1RSW -0.0307934329
## City1SAN -0.0061451135
## City1SAT 0.0032930690
## City1SDF -0.0005128854
## City1SEA -0.0040366306
## City1SFO -0.0079990608
## City1SJC -0.0005443619
## City1SLC -0.0164703569
## City1SMF -0.0062943786
## City1SNA 0.0022917603
## City1STL -0.0050383835
## City1SYR .
## City1TLH -0.0009670047
## City1TPA -0.0133742040
## City1TYS 0.0044308635
## City2ATL 0.0064366122
## City2AUS .
## City2BDL 0.0134063821
## City2BHM 0.0004287506
## City2BNA -0.0081328870
## City2BOS 0.0039392375
## City2BUF -0.0076288718
## City2BWI 0.0036137215
## City2CLE 0.0123223738
## City2CLT 0.0114999844
## City2CMH 0.0092817482
## City2CVG 0.0313164102
## City2DAY -0.0065573054
## City2DEN 0.0034510908
## City2DFW 0.0080155963
## City2DSM 0.0011788219
## City2DTW 0.0020038795
## City2ELP -0.0124443559
## City2FLL -0.0112091421
## City2FNT -0.0016701691
## City2GEG -0.0094572740
## City2GPT -0.0018604922
## City2GSO -0.0114625131
## City2HPN 0.0033211277
## City2HRL -0.0034180736
## City2IAD 0.0420445129
## City2IAH 0.0112398942
## City2ICT -0.0007161067
## City2IND 0.0042906480
## City2ISP -0.0009110221
## City2JAN -0.0110482815
## City2JAX -0.0154144033
## City2LAS -0.0216152621
## City2LAX 0.0063071586
## City2LBB -0.0133377211
## City2LGA 0.0344256657
## City2LGB -0.0008074996
## City2LIT -0.0037659357
## City2MAF -0.0064610468
## City2MCI -0.0107603353
## City2MCO -0.0158817462
## City2MDT 0.0043956930
## City2MEM 0.0040747038
## City2MHT -0.0110384177
## City2MIA 0.0039551006
## City2MKE 0.0041934024
## City2MSP 0.0396108706
## City2MSY -0.0140079307
## City2OAK 0.0036537974
## City2OKC -0.0084846083
## City2OMA -0.0023741086
## City2ONT -0.0081676351
## City2ORD 0.0077188611
## City2ORF 0.0004043445
## City2PBI -0.0152030820
## City2PDX 0.0041259617
## City2PHF -0.0032518420
## City2PHL 0.0123650341
## City2PHX -0.0162939205
## City2PIT 0.0040086185
## City2PNS .
## City2PVD -0.0020945906
## City2PWM 0.0037964066
## City2RDU -0.0193152497
## City2RIC 0.0060977133
## City2RNO -0.0101499368
## City2ROC 0.0009094769
## City2RSW -0.0141646308
## City2SAN -0.0089620527
## City2SAT 0.0008734934
## City2SAV .
## City2SDF 0.0013733821
## City2SEA -0.0015654822
## City2SFO 0.0151350367
## City2SJC -0.0044996930
## City2SLC 0.0022841062
## City2SMF -0.0073545316
## City2SNA -0.0002424170
## City2SRQ -0.0079041518
## City2STL 0.0032713699
## City2SYR -0.0054724704
## City2TLH -0.0096139360
## City2TPA -0.0142616046
## City2TUL -0.0053297435
## City2TUS -0.0067311090
## Market.Leading.Airline..MLA.AA 0.0312395997
## Market.Leading.Airline..MLA.AS -0.0098739908
## Market.Leading.Airline..MLA.B6 -0.0021601156
## Market.Leading.Airline..MLA.CO -0.0075955212
## Market.Leading.Airline..MLA.DL 0.0100481850
## Market.Leading.Airline..MLA.F9 0.0160671565
## Market.Leading.Airline..MLA.FL -0.0029673762
## Market.Leading.Airline..MLA.G4 0.0070059522
## Market.Leading.Airline..MLA.HP -0.0077112292
## Market.Leading.Airline..MLA.NK 0.0096713262
## Market.Leading.Airline..MLA.NW 0.0220870071
## Market.Leading.Airline..MLA.TZ 0.0083668672
## Market.Leading.Airline..MLA.UA -0.0048497260
## Market.Leading.Airline..MLA.US 0.0091243117
## Market.Leading.Airline..MLA.WN -0.0436411415
## Market.Leading.Airline..MLA.YX -0.0104569660
## Low.Price.Airline..LPA.AA -0.0058565938
## Low.Price.Airline..LPA.AQ -0.0046958776
## Low.Price.Airline..LPA.AS 0.0005642309
## Low.Price.Airline..LPA.AX 0.0117490215
## Low.Price.Airline..LPA.B6 -0.0110748131
## Low.Price.Airline..LPA.CO 0.0125673300
## Low.Price.Airline..LPA.DL 0.0062989999
## Low.Price.Airline..LPA.F9 0.0002858695
## Low.Price.Airline..LPA.FL -0.0353375060
## Low.Price.Airline..LPA.G4 0.0068994097
## Low.Price.Airline..LPA.HP 0.0088809877
## Low.Price.Airline..LPA.NK -0.0083659405
## Low.Price.Airline..LPA.NW 0.0042527024
## Low.Price.Airline..LPA.SY 0.0003067789
## Low.Price.Airline..LPA.TZ 0.0043285101
## Low.Price.Airline..LPA.UA 0.0100185054
## Low.Price.Airline..LPA.US 0.0133980821
## Low.Price.Airline..LPA.WN -0.0068112713
## Low.Price.Airline..LPA.YX 0.0007845976#Lasso regression for Model 1
lasso_reg <- glmnet(X_train, Y_train, alpha = 1, lambda = lambda)
# Find the best lambda via cross-validation
lasso_reg1 <- cv.glmnet(X_train, Y_train, alpha = 1)
bestlam <- lasso_reg1$lambda.min
# Predict on the validation set
lasso.pred <- predict(lasso_reg, s = bestlam, newx = X_cv)
# Calculate mean squared error
mse <- mean((Y_cv - lasso.pred)^2)
print(paste("Mean Squared Error:", mse))## [1] "Mean Squared Error: 0.0292821348606005"# Calculate R2 value
sst <- sum((Y_cv - mean(Y_cv))^2)
sse <- sum((Y_cv - lasso.pred)^2)
r2 <- 1 - (sse / sst)
print(paste("R²:", r2))## [1] "R²: 0.971221917810644"# Get the Lasso regression coefficients
lasso.coef <- predict(lasso_reg, type = "coefficients", s = bestlam)
print("Lasso Coefficients:")## [1] "Lasso Coefficients:"print(lasso.coef)## 218 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 1.233455e-03
## (Intercept) .
## Distance 1.202526e-02
## Average.Weekly.Passengers -1.076574e-02
## Market.Share.MLA .
## Average.MLA.Fare 8.426525e-01
## Market.Share.LPA -1.289130e-02
## Average.LPA.Fare 1.434164e-01
## City1ABQ .
## City1ACY .
## City1ALB .
## City1AMA .
## City1ATL .
## City1AUS .
## City1BDL .
## City1BHM .
## City1BNA .
## City1BOI .
## City1BOS .
## City1BTV .
## City1BUF .
## City1BUR .
## City1BWI .
## City1CAE .
## City1CAK .
## City1CHS .
## City1CLE .
## City1CLT .
## City1CMH -2.049263e-03
## City1COS .
## City1CRP .
## City1CVG 3.512094e-04
## City1DAY .
## City1DEN .
## City1DFW .
## City1DSM .
## City1DTW .
## City1ELP .
## City1EUG .
## City1FLL .
## City1FNT .
## City1GPT .
## City1GRR .
## City1GSO .
## City1GSP .
## City1HRL .
## City1IAD .
## City1IAH .
## City1IND .
## City1ISP .
## City1JAX .
## City1LAS .
## City1LAX .
## City1LGA .
## City1LGB .
## City1LIT .
## City1MCI .
## City1MCO .
## City1MDT .
## City1MEM .
## City1MHT .
## City1MIA .
## City1MKE .
## City1MSN .
## City1MSP .
## City1MSY .
## City1MYR .
## City1OAK .
## City1OKC .
## City1OMA .
## City1ONT .
## City1ORD 5.392241e-03
## City1ORF .
## City1PDX .
## City1PHF .
## City1PHL 2.253072e-03
## City1PHX .
## City1PIT .
## City1PSP .
## City1PVD .
## City1RDU .
## City1RNO .
## City1ROC .
## City1RSW .
## City1SAN .
## City1SAT 1.185846e-03
## City1SDF .
## City1SEA .
## City1SFO .
## City1SJC -2.054821e-03
## City1SLC .
## City1SMF .
## City1SNA .
## City1STL .
## City1SYR .
## City1TLH .
## City1TPA .
## City1TYS .
## City2ATL .
## City2AUS .
## City2BDL .
## City2BHM .
## City2BNA .
## City2BOS .
## City2BUF .
## City2BWI .
## City2CLE .
## City2CLT .
## City2CMH .
## City2CVG 3.479620e-03
## City2DAY .
## City2DEN .
## City2DFW .
## City2DSM .
## City2DTW .
## City2ELP .
## City2FLL .
## City2FNT .
## City2GEG .
## City2GPT .
## City2GSO .
## City2HPN .
## City2HRL .
## City2IAD .
## City2IAH .
## City2ICT .
## City2IND .
## City2ISP .
## City2JAN .
## City2JAX .
## City2LAS .
## City2LAX .
## City2LBB .
## City2LGA .
## City2LGB .
## City2LIT .
## City2MAF .
## City2MCI .
## City2MCO .
## City2MDT .
## City2MEM .
## City2MHT .
## City2MIA .
## City2MKE .
## City2MSP .
## City2MSY .
## City2OAK .
## City2OKC .
## City2OMA .
## City2ONT .
## City2ORD .
## City2ORF .
## City2PBI .
## City2PDX .
## City2PHF .
## City2PHL .
## City2PHX -2.554718e-03
## City2PIT .
## City2PNS .
## City2PVD .
## City2PWM 2.854612e-03
## City2RDU -2.966510e-03
## City2RIC .
## City2RNO .
## City2ROC .
## City2RSW .
## City2SAN .
## City2SAT .
## City2SAV .
## City2SDF .
## City2SEA .
## City2SFO .
## City2SJC .
## City2SLC .
## City2SMF .
## City2SNA .
## City2SRQ .
## City2STL .
## City2SYR .
## City2TLH .
## City2TPA .
## City2TUL .
## City2TUS .
## Market.Leading.Airline..MLA.AA 3.634147e-03
## Market.Leading.Airline..MLA.AS .
## Market.Leading.Airline..MLA.B6 2.231912e-04
## Market.Leading.Airline..MLA.CO -2.437281e-03
## Market.Leading.Airline..MLA.DL .
## Market.Leading.Airline..MLA.F9 6.514889e-03
## Market.Leading.Airline..MLA.FL .
## Market.Leading.Airline..MLA.G4 3.791177e-03
## Market.Leading.Airline..MLA.HP .
## Market.Leading.Airline..MLA.NK .
## Market.Leading.Airline..MLA.NW .
## Market.Leading.Airline..MLA.TZ .
## Market.Leading.Airline..MLA.UA -3.185118e-02
## Market.Leading.Airline..MLA.US .
## Market.Leading.Airline..MLA.WN -1.403704e-02
## Market.Leading.Airline..MLA.YX -1.445743e-04
## Low.Price.Airline..LPA.AA .
## Low.Price.Airline..LPA.AQ .
## Low.Price.Airline..LPA.AS .
## Low.Price.Airline..LPA.AX .
## Low.Price.Airline..LPA.B6 .
## Low.Price.Airline..LPA.CO 6.048242e-03
## Low.Price.Airline..LPA.DL .
## Low.Price.Airline..LPA.F9 .
## Low.Price.Airline..LPA.FL -7.513595e-03
## Low.Price.Airline..LPA.G4 3.164509e-05
## Low.Price.Airline..LPA.HP .
## Low.Price.Airline..LPA.NK .
## Low.Price.Airline..LPA.NW .
## Low.Price.Airline..LPA.SY .
## Low.Price.Airline..LPA.TZ .
## Low.Price.Airline..LPA.UA .
## Low.Price.Airline..LPA.US 6.158888e-03
## Low.Price.Airline..LPA.WN .
## Low.Price.Airline..LPA.YX .#Ridge Regression for Model 2
# Load necessary libraries
names(air)## [1] "Average.Flight.Fare" "Distance"
## [3] "Average.Weekly.Passengers" "Market.Share.MLA"
## [5] "Average.MLA.Fare" "Market.Share.LPA"
## [7] "Average.LPA.Fare"# View the first few rows of the dataset
head(air)## Average.Flight.Fare Distance Average.Weekly.Passengers Market.Share.MLA
## 1 -0.9368159 -0.8505674 -0.2338755 0.5154121
## 2 -0.7897959 -0.3327826 -0.6833202 0.7708175
## 3 0.9000153 -0.3452593 -0.8691588 0.9679634
## 4 -1.7650899 -1.2248697 0.3207194 1.9084378
## 5 -0.1344543 -0.5464468 -0.5731801 -1.0659165
## 6 -0.5564017 0.2037174 -0.9200910 -1.0196210
## Average.MLA.Fare Market.Share.LPA Average.LPA.Fare
## 1 -0.9379300 1.3351633 -0.735564651
## 2 -0.7417904 -0.6291264 -0.565193579
## 3 0.8990496 -1.1654487 0.472540712
## 4 -1.6237683 2.3284352 -1.643851390
## 5 -0.1193803 -0.6992266 0.005151045
## 6 -0.4997872 -0.6343190 -0.376730004# Create model matrix
X <- model.matrix(Average.Flight.Fare ~ ., air)
# Separate the target variable
Y <- air$Average.Flight.Fare
# Define the lambda sequence
lambda <- 10^seq(10, -2, length = 100)
print(lambda)## [1] 1.000000e+10 7.564633e+09 5.722368e+09 4.328761e+09 3.274549e+09
## [6] 2.477076e+09 1.873817e+09 1.417474e+09 1.072267e+09 8.111308e+08
## [11] 6.135907e+08 4.641589e+08 3.511192e+08 2.656088e+08 2.009233e+08
## [16] 1.519911e+08 1.149757e+08 8.697490e+07 6.579332e+07 4.977024e+07
## [21] 3.764936e+07 2.848036e+07 2.154435e+07 1.629751e+07 1.232847e+07
## [26] 9.326033e+06 7.054802e+06 5.336699e+06 4.037017e+06 3.053856e+06
## [31] 2.310130e+06 1.747528e+06 1.321941e+06 1.000000e+06 7.564633e+05
## [36] 5.722368e+05 4.328761e+05 3.274549e+05 2.477076e+05 1.873817e+05
## [41] 1.417474e+05 1.072267e+05 8.111308e+04 6.135907e+04 4.641589e+04
## [46] 3.511192e+04 2.656088e+04 2.009233e+04 1.519911e+04 1.149757e+04
## [51] 8.697490e+03 6.579332e+03 4.977024e+03 3.764936e+03 2.848036e+03
## [56] 2.154435e+03 1.629751e+03 1.232847e+03 9.326033e+02 7.054802e+02
## [61] 5.336699e+02 4.037017e+02 3.053856e+02 2.310130e+02 1.747528e+02
## [66] 1.321941e+02 1.000000e+02 7.564633e+01 5.722368e+01 4.328761e+01
## [71] 3.274549e+01 2.477076e+01 1.873817e+01 1.417474e+01 1.072267e+01
## [76] 8.111308e+00 6.135907e+00 4.641589e+00 3.511192e+00 2.656088e+00
## [81] 2.009233e+00 1.519911e+00 1.149757e+00 8.697490e-01 6.579332e-01
## [86] 4.977024e-01 3.764936e-01 2.848036e-01 2.154435e-01 1.629751e-01
## [91] 1.232847e-01 9.326033e-02 7.054802e-02 5.336699e-02 4.037017e-02
## [96] 3.053856e-02 2.310130e-02 1.747528e-02 1.321941e-02 1.000000e-02# Split the data into training and validation sets
set.seed(123)
part <- sample(2, nrow(X), replace = TRUE, prob = c(0.7, 0.3))
X_train <- X[part == 1, ]
X_cv <- X[part == 2, ]
Y_train <- Y[part == 1]
Y_cv <- Y[part == 2]
# Perform Ridge regression
ridge_reg <- glmnet(X_train, Y_train, alpha = 0, lambda = lambda)
summary(ridge_reg)## Length Class Mode
## a0 100 -none- numeric
## beta 700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric# Find the best lambda via cross-validation
ridge_reg1 <- cv.glmnet(X_train, Y_train, alpha = 0)
bestlam <- ridge_reg1$lambda.min
print(bestlam)## [1] 0.09578835# Predict on the validation set
ridge.pred <- predict(ridge_reg, s = bestlam, newx = X_cv)
# Calculate mean squared error
mse <- mean((Y_cv - ridge.pred)^2)
print(paste("Mean Squared Error:", mse))## [1] "Mean Squared Error: 0.035861413515817"# Calculate R2 value
sst <- sum((Y_cv - mean(Y_cv))^2)
sse <- sum((Y_cv - ridge.pred)^2)
r2 <- 1 - (sse / sst)
print(paste("R²:", r2))## [1] "R²: 0.967367833682311"# Get the Ridge regression coefficients
ridge.coef <- predict(ridge_reg, type = "coefficients", s = bestlam)
print("Ridge Coefficients:")## [1] "Ridge Coefficients:"print(ridge.coef)## 8 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 0.006104435
## (Intercept) .
## Distance 0.054113759
## Average.Weekly.Passengers -0.021209760
## Market.Share.MLA 0.043969523
## Average.MLA.Fare 0.633000044
## Market.Share.LPA -0.072504226
## Average.LPA.Fare 0.281564539#Lasso regression for Model 2
lasso_reg <- glmnet(X_train, Y_train, alpha = 1, lambda = lambda)
# Find the best lambda via cross-validation
lasso_reg1 <- cv.glmnet(X_train, Y_train, alpha = 1)
bestlam <- lasso_reg1$lambda.min
# Predict on the validation set
lasso.pred <- predict(lasso_reg, s = bestlam, newx = X_cv)
# Calculate mean squared error
mse <- mean((Y_cv - lasso.pred)^2)
print(paste("Mean Squared Error:", mse))## [1] "Mean Squared Error: 0.027161488860179"# Calculate R2 value
sst <- sum((Y_cv - mean(Y_cv))^2)
sse <- sum((Y_cv - lasso.pred)^2)
r2 <- 1 - (sse / sst)
print(paste("R²:", r2))## [1] "R²: 0.975284347853983"# Get the Lasso regression coefficients
lasso.coef <- predict(lasso_reg, type = "coefficients", s = bestlam)
print("Lasso Coefficients:")## [1] "Lasso Coefficients:"print(lasso.coef)## 8 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 0.0061523685
## (Intercept) .
## Distance 0.0145426104
## Average.Weekly.Passengers -0.0180372510
## Market.Share.MLA 0.0005508776
## Average.MLA.Fare 0.8118474834
## Market.Share.LPA -0.0121903117
## Average.LPA.Fare 0.1769229906